The formation of numerous channels among Arctic sea ice provides potential routes for Arctic navigation and the identification and semantic segmentation of sea ice becomes a crucial task. This letter proposes a sea ice semantic segmentation method with adaptive training sample selection and cross-attention mechanism to enhance the robustness under the complex climatic conditions of the Arctic. First, the image is divided into patches. An adaptive iterative clustering on them automatically selects the training samples. Second, ResUNet with a cross-attention mechanism is used for image segmentation. This approach enhances contextual understanding with relatively low computational overhead, enabling better focus on relevant features across different layers of the network. The experimental results demonstrate that the proposed method can achieve high accuracy segmentation with a small training set. Furthermore, the proposed method exhibits segmentation consistency across two datasets and various types of sea ice.

In this article, we explore the potential of quantum machine learning for subsurface feature extractions from radar sounder signals. We propose a hybrid quantum-classical learning paradigm that leverages parameterized quantum circuits to generate probability amplitudes based on quantum properties such as superposition and entanglement. These amplitudes are synergistically integrated with the classical deep neural networks that are efficient in learning high-dimension contextual features for downstream prediction tasks. The present research work is structured around two objectives. First, we investigate the role of quantum circuits in the latent space for transferring back-and-forth rich discriminative spatial context from the encoder to the decoder for segmentation. Second, we investigate how the probabilistic amplitudes derived from quantum circuits are significant in integrating into the classical models to provide new insights for radar sounder signals segmentation. The performance of the hybrid architectures has been studied in small-scale settings by simulating the expected behaviour of the quantum circuits on a classical machine. The experimental results have demonstrated the viability of quantum machine learning frameworks on MCoRDS-1 and MCoRDS-3 datasets for radar sounder signal segmentation. Qualitatively, they are capable of delineating the spatial extent of the bedrock from noise. Additionally, we conduct a comparative analysis between theQiskit Aer Simulatorand theIBM FakeBackend Simulatorto highlight the computational trade-offs and validate fidelity of two simulators for scalable experimentation. Therefore, our work opens up new avenues of research for future radar sounder data analysis leading to more precise and efficient subsurface target segmentation.

Change detection (CD) in Satellite Image Time Series (SITS) is more complex than in bi-temporal images due to the higher dimensionality of the data. Utilizing the full dimensionality of the time series remains challenging, particularly with dense SITS. An approach that can minimize dimensions without compromising informational depth is essential. In this paper, we present an innovative framework for Change Vector Analysis (CVA) in time series analysis and initial demonstrations of its effectiveness in capturing the spectral-temporal characteristics of changes. Unlike current methods, the proposed approach incorporates a wide range of spectral-temporal information and constructs separate reference matrices for each change type, facilitating an in-depth analysis of change components for CD. Based on the Time Series Change Vector (TSCV), the proposed framework extends CVA into the time series perspective, offering novel interpretations for magnitude and direction across temporal and spectral dimensions. The framework effectiveness is validated using Sentinel-2 data, demonstrating significant improvements in tackling multiple CD challenges in dense SITS scenarios.

Change detection (CD) is one of the essential tasks in Remote Sensing applications. Deep Learning (DL) methods for CD are typically categorized as either bitemporal or multitemporal, and methods focus on one kind of task. In this paper, we propose a dual-branch supervised CD method which uses time series of Remote Sensing optical data to simultaneously perform two tasks, identifying binary abrupt changes and multitemporal seasonal ones. The method relies on a 3D fully convolutional architecture and uses dilated convolutions as well as spatial and channel attention mechanisms to examine the spatial and temporal dimensions of the data. The method is tested on a time series of multispectral images acquired by Landsat-8. Results show that the proposed framework proves effective in detecting the two kinds of changes.

In the radar sounder literature, extracting subsurface geological information relies on supervised deep learning with large labeled datasets. While some methods reduce the need for extensive labels through weak supervision, there remains a gap in the availability of unsupervised segmentation techniques. This paper proposes a novel method for unsupervised radargram segmentation based on incremental learning (IL). The method involves the prior geophysical modeling of the cryosphere subsurface targets into a class hierarchy. Through several IL steps, a network is trained to progressively extract semantically meaningful features that are analyzed to compute the segmentation map. Each step refines the segmentation map by considering the new targets from the following level of the class hierarchy that details the targets at the previous level. To enhance the training process, contrastive learning is incorporated, along with techniques for distilling information from prior iterations to recall the network the properties of previously seen classes. To validate the effectiveness of the proposed method, we conducted successful experiments on MCoRDS-3 data acquired in Greenland.

This article presents a hybrid quantum-classical framework by incorporating quantum feature maps regulated classical Convolutional Neural Network (CNN) architecture in the context of detecting different subsurface targets in the radar sounder signal. The quantum feature maps are generated by quantum circuits to utilize spatially-bound input information from the input training samples. The associated spectral probabilistic amplitudes of the feature maps are further fed as an input to the classical CNN-based network to classify the subsurface targets in the radargram. Experimental results on the MCoRDS and MCoRDS3 dataset demonstrated the capability of contextualizing the classical architecture through quantum feature maps for characterizing the radar sounder data.

Detecting various types of changes in dense Satellite Image Time Series (SITS) presents a complex challenge. While Change Vector Analysis (CVA) is widely used for Change Detection (CD), it presents limitations due to a lack of prior information on changes, such as optimal spectral channels and change timing. To overcome these obstacles, the study focuses on the direction analysis of the Time Series Change Vectors (TSCV) [1], built upon CVA principles. Conducting unsupervised CD using time series magnitude information, the approach leverages multiple change dimensions in the direction analysis. A novel scheme formulates a representative change matrix within SITS temporal and spectral domains, guiding change representations and allowing segregation based on significance in both spectral and temporal dimensions. The proposed method efficacy is evaluated using Sentinel-2 time series data, with results affirming its robustness in effectively addressing multi-CD challenges within dense SITS.

The advent of new satellite missions offering high spatial, spectral, and temporal resolution has significantly enhanced the possibility to monitor vegetation and agricultural practices. The High-resolution (HR) Satellite Image Time Series (SITS) enables a deeper understanding of crop fields behavior and precise boundary detection. While Convolutional Neural Networks (CNNs) have demonstrated effectiveness in crop fields-related analyses, existing methods for crop boundary detection often focus on mono-temporal image analysis, overlooking valuable multi-temporal information in SITS. To address this gap, we propose the utilization of a UNet-based three-dimensional (3D) CNN architecture, allowing for the simultaneous modeling of spatial-temporal information within multi-spectral multi-temporal SITS. Additionally, we explore various CNN-based U-Net models to further validate the proposed approach in accurately detecting crop field boundaries. The method is evaluated in an agricultural area in Germany using 12 Sentinel-2 Level-2A images and has demonstrated promising results.

The rapid melting of Arctic sea ice presents significant opportunities and challenges for humanity. The formation of numerous channels between the ice offers potential for Arctic navigation. The identification and semantic segmentation of sea ice is a crucial task in sea ice monitoring. To reduce the influence of complex climatic conditions in the Arctic region on the robustness of the sea ice segmentation model, this paper proposes a sea ice semantic segmentation model based on adaptive training sample selection on U-Net for Sentinel-2 data. The method adaptively selects training samples through unsupervised iterative clustering and inputs them into U-Net network for image segmentation. In addition, the iterative efficiency of clustering is improved by building subspaces. The experimental results on Sentinel-2 data show that the proposed method can effectively achieve sea ice segmentation with a high positive detection rate and low false alarm rate.

Climate change finds one of its main causes in the happening transition between forest and arid lands of different types as a consequence of wildfires and/or massive deforestation practices. Remote sensing should provide effective and scalable solutions to monitor this dangerous trend. In this paper, a recently developed novel model for one-class classification based on abstract features constructed from normalized difference indices is presented and challenged on detecting deforestation patterns on multispectral images. Results are promising as the performance is nearly optimal, showing that the model comes with good generalization capabilities to deal with vegetation related changes in general.

In recent years, deep learning methods, in particular Convolutional Neural Networks (CNNs), have been increasingly used in Change Detection (CD). However, most CNN-based CD methods are primarily designed for analyzing only a single pair of images due to the challenge of collecting and constructing ground reference data during the system-training phase. Consequently, existing CD methods, particularly those focused on detecting multi-annual changes, exhibit limited capability in extracting comprehensive spatio-temporal information. To address this limitation, we propose a novel weakly supervised deep learning-based technique for CD exploiting a 3D CNN architecture to extract spatio-temporal information. Our technique incorporates a fine-tuning stage to effectively capture temporal patterns from a yearly Satellite Image Time Series (SITS) by using different 3D convolutional layers. It also exploits a multi-feature hyper-temporal Change Vector Analysis (CVA) for multi-annual change identification. The proposed approach is tested on a four year dataset in Amazonia and gained the highest yearly CD accuracy of 88.59%, 97.27% and 87.87% for 2017, 2018 and 2019, respectively.

Deep learning (DL) approaches are widely used to improve change detection (CD). In many application domains, unsupervised DL CD methods are preferred since gathering multitemporal labeled samples is challenging. Many unsupervised CD methods use pre-trained DL models to extract multiscale features. This does not allow for preserving the spatial context information and the object structure in the multiscale hidden-layer features, thus obtaining poor performance in modeling multiresolution changes. In this article, we propose two hierarchical loss functions to train multiscale hidden-layer features and preserve their spatial context information. The multiscale hidden-layer feature maps extracted from the model are used in an unsupervised multiscale CD method. We present two possible hierarchical loss functions. The first one considers all the model layers during the training by comparing the mirrored couples of encoder–decoder hidden-layer features, while the second one aims to preserve the geometrical details using a multiresolution-based loss function. After training, the CD method uses a feature selection (FS) based on structure-similarity-index (SSIM) to keep only the most informative hidden-layer feature maps. We tested the proposed method on bi-temporal multispectral images acquired by Landsat-8 representing a burned area and Sentinel-2 images representing a deforested area.

Radar sounder (RS) profiles are essential for imaging the subsurface of planetary bodies and the Earth as they provide valuable geological insights. However, the limited availability of high-resolution radargrams poses challenges. This article proposes a novel method based on generative models to super-resolve radargrams. Our approach addresses the ill-posed and ill-conditioned nature of the super-resolution problem by training a neural network to learn the correlation between radargrams at different scales. The network learns a proxy for the mapping function between ambiguous low-resolution radargrams and more detailed high-resolution ones, considering the data’s geological and statistical properties. The mapping function enables the super-resolution of previously unseen low-resolution radargrams acquired in comparable conditions to those in the training and imaging similar underlying geology. To achieve this, we adopt a cycle generative adversarial network (CycleGAN), explicitly designed to match properties between low- and high-resolution radargrams, accounting for variations in dimensions and radiometric properties. Furthermore, we enhance the network performance by incorporating skip connections, a ResNet module, and attention mechanisms. The proposed method is validated using MCoRDS3 radargrams acquired in Greenland and Antarctica as high-resolution data. As low-resolution data, we used simulated radargrams representing what is expected by an Earth-orbiting low-resolution RS to have a controlled experiment. The results are evaluated qualitatively and quantitatively, focusing on the areas with reflections with complex shapes that may generate artifacts and unrealistic geological features.

High-resolution (HR) satellite image time series (SITS) are a valuable data source for analyzing land cover change (LCC) due to their large amount of spatial, spectral, and temporal information. However, most existing LCC detection methods focus on binary change detection (CD) within a single year and fail to provide detailed information about the specific type of change. In this study, we propose a multiannual CD approach that identifies changes occurring between consecutive years and provides information about the type of LC transition. The proposed approach exploits multiannual and multispectral SITS to generate a hypertemporal feature space (FS). This FS is analyzed to create a set of CD maps that indicate the time, probability, and type of change. To measure the similarity between pixel time series, we use dynamic time warping (DTW) in the space of hypertemporal features. A hierarchical clustering technique is exploited to develop a set of class prototypes (CPs) that represent the characteristics of different LC classes. The CPs are then used to identify the most probable LC transition for each changed pixel. Two test areas were selected to evaluate the effectiveness of the proposed approach. The first one is located in Amazon and spans the years 2015 to 2019; and the second one is located in Sahel-Africa and covers the years 2015 and 2016, using multiannual Landsat 7 and 8 SITS. The results demonstrate that the proposed approach is effective in detecting multiannual changes and in identifying the LC transitions.

In the literature, Change Detection (CD) methods use the information from heterogeneous sensors to detect the changes more effectively with respect to single-sensor methods. Among them, Deep Learning (DL) CD methods try to learn a common feature domain or perform a domain adaptation, but many still present domain gaps between multi-sensor feature maps. We propose a DL CD method that learns multiscale feature maps in a domain common to multi-modal SAR images using a Cycle Generative Adversarial Network (CycleGAN). We apply a code-alignment loss function to reduce the domain gap between the feature maps derived from the multi-sensor SAR images. The multi-scale feature maps of the CycleGAN generators are processed to derive the change map. Preliminary experiments performed by processing bi-temporal SAR images acquired from COSMO-SkyMed and SAOCOM showed promising results.

Analyzing radar sounder (RS) profiles allows the retrieval of critical information on subsurface geology. However, radar-grams suffer from several noise contributions, adversely affecting the data quality and reliability. In the remote sensing literature, there are no methods for denoising radargrams, and those for optical data denoising and SAR and GPR data de-speckling are based on assumptions that are not valid in the RS domain. This paper analyses the statistical distributions of the noisy contributions in radargrams at different levels of processing. It proposes a novel method to denoise complex raw radargrams using a generative network (diffusion probabilistic model) that learns the noise statistical properties. The model is iteratively trained to learn the information loss as a function of the noise level increment in the data. By reversing the process, the network estimates the noise statistical properties and denoises unseen radargrams. The method has been successfully validated on the raw Experiment Data Record (EDR) radargrams of Mars that the Shallow Radar (SHARAD) acquired.

The ice sheet dynamics in Antarctica that directly impact the polar ice mass balance and the glacier erosion caused to the bedform are predicted by models that rely on several hard-to-estimate variables, including the bed topography itself. Antarctica’s bed topography is hard to estimate because it is covered by several layers of ice that could be up to several kilometers thick. Sparse, higher-resolution along-track measurements of its bed topography collected using Ice-Penetrating Radar (IPR) data are interpolated to create coarser-resolution gridded bed topography models. However, the significant gaps between IPR profiles mean there is significant scope for improving the measurements and interpolation approaches to fill those gaps. Here, we propose a deep learning (DL) generative adversarial network (GAN) approach to generate a realistic model of the bed topography from single-channel IPR acquisitions. The model takes advantage of the clutter caused by the IPR antenna to predict a Digital Elevation Model (DEM) accordingly to the information in the IPR acquisitions. The method is tested with synthetic data from regions with a high-resolution DEM available.

Unsupervised semantic segmentation is the method of discovering meaningful semantic contents within the image domain without using any labelled information. The learned semantic contents are then decomposed into distinct semantic segments with known ontology. The core task of an unsupervised feature learning algorithm is to produce dense features for every pixel with rich semantic content to form distinct clusters with compact information for the downstream task. In this work, we extend the previously developed Self-Supervised Transformer with Energy-based Graph Optimization (STEGO) architecture by integrating a convolution-based Expansive Network in the decoder along with the spatial similarity loss function for radar sounder signal segmentation. Experimental results on the Multi-Channel Coherent Radar Depth Sounder (MCoRDS) data confirm the capability of the proposed unsupervised segmentation method.

A great effort has been put on developing technologies that can process High Resolution (HR) satellite datasets to properly monitor the environmental changes and produce long term Change Detection (CD) maps. However, there is still a need to design CD approaches that process Satellite Image Time Series (SITS) with high spatial, spectral, and temporal resolution and describe changes that have occurred between the consecutive years. Here, a CD processing chain is proposed that: i) extracts several relevant features of the spectral trends of different sets of LC changes, ii) produces a regular and dense feature time series, iii) analyzes differences between the consecutive years by using a Multi-feature Hyper-temporal Change Vector Analysis (MHCVA) technique, and iv) detects the year and the probability of changes at pixel level. The effectiveness of the proposed approach is tested on a multi-annual Landsat 7 and 8 images of an area located in Amazon.

Automated road segmentation is considered an essential aspect of the development and planning of cities. However, automatically extracting road information from remote sensing imagery with manual labeling is still challenging due to the road network structures diversity. We propose a deep learning method that is joint learning from very high-resolution remote sensing images and volunteer geographic information. The method can handle complex scenarios. We modified the channel attention residual U-Net model with a weighted loss technique to address the problem of unbalanced training data and increase accuracy. Experimental results indicate that U-Net, Residual U-Net, and Attention U-Net achieved an overall accuracy of 0.91%, 0.93%, and 0.97%, respectively, and the proposed method with 0.99% overall accuracy has the best performance within the tested U-Net series.

Airborne Radar Sounders (RSs) are active sensors that acquire subsurface data for Earth observation. RS data (radargrams) provide information on buried geology by imaging subsurface dielectric discontinuities. Recently, several automatic RS target identification techniques have been proposed, being convolutional neural network (CNN)-based methods the most promising. However, they require numerous labeled data that are hard to retrieve in the subsurface environment targeted by RS. Further, they are not designed to effectively deal with problems showing unbalanced classes like RS segmentation. We introduce newer cryosphere subsurface targets in the inland and coastal areas that can have a very low probability. To deal with the higher complexity and variability than previous works, we propose a transfer learning framework for RS data to mitigate the need for a large amount of labeled data and handle extremely unbalanced target classes. Herewith, we propose two transfer learning-based mechanisms for radargram segmentation. The first uses a lightweight architecture whose pre-training is supervised with a large labeled dataset from other domains. The second mechanism uses a deep architecture pre-trained in the RS domain, considering the pretest task of radargram reconstruction. The architectures are modified to deal with the characteristics of RS data and the radargram segmentation task. Finally, both methods are fine-tuned with a few labeled radargrams to learn radargram features useful for segmentation. We reveal experimental results on radargrams acquired in Antarctica by MCoRDS-1 and MCoRDS-3. The results demonstrate the effectiveness of transfer learning for radargram segmentation.

Image registration is the most basic preprocessing method used to unify coordinates among multitemporal very-high-resolution (VHR) satellite images, thus allowing the acquisition of reliable data of the Earth’s surface. Although image registration requires multiple matching points (MP), false matching points (FMP) are included because of the similar spectral patterns and noise. However, removing FMPs from VHR satellite image pairs is challenging, especially when the images are directly affected by complex factors, such as shadow, relief displacement, and terrain shielding. Therefore, we propose a false matching point removal network (FMPR-net) based on deep learning to eliminate effectively the FMPs to improve registration accuracy. The training dataset is produced by a semi-automatic method. It involves the generation of image patch pairs based on a matching process of scale-invariant feature transform and the assignment of labels referring to the characteristics of true matching points (TMP) and FMPs. The FMPR-net is designed in a Siamese format consisting of two matching point deep feature extractors (MDFE). The architecture of the MDFE consists of one main network and three branch networks to achieve robust extraction of meaningful deep features describing the characteristics of MPs. The FMPR-net removes the FMPs using a true matching probability calculated based on the similarity between deep features. Experiments conducted on four pairs of VHR satellite images have demonstrated that the FMPR-net can effectively remove the FMPs. Consequently, accurate VHR satellite image registration is possible by reducing uncertainty caused by the FMPs.

We propose an unsupervised methodology for multi-class change detection (CD) in multimodal remote sensing data fused using the Kronecker product formalism. The method utilizes the compressed change vector analysis (C2VA) on the fully vectorized change matrices. The multimodal case is demonstrated using dual-frequency full-polarimetric Syn-thetic Aperture Radar (SAR) data obtained by EMISAR over the Foulum agricultural area. The change types are inves-tigated using ground truth data for the growth of various crops. The work showcases the capability of the Kronecker product-based CD formalism beyond conventional scalar change indices.

Radar sounders (RSs) are widely used to image profiles (radargrams) of the subsurface of planetary bodies and the Earth. However, despite the huge scientific return from radargram analyses, their horizontal and vertical resolutions are limited by technical factors. Even if methods exist for improving the resolution, these are still limited by technical factors and introduce artifacts. This paper proposes an unsupervised deep-learning method that synthesizes accurate super-resolved radargrams overcoming these limitations. The method adopts the Cycle-Consistent Adversarial Network (CyleGAN) that learns the mapping function between the low- and high-resolution data distributions. The network is adapted to match the low- and high-resolution radargram characteristics, including the differences in dimensions and radiometric properties. The proposed method was successfully validated on airborne data at higher resolution and simulated data with lower resolution.

Radar Sounders (RSs) are space-borne and airborne sensors operating on the nadir-looking geometry to collect sub-surface information by transmitting linearly modulated electro-magnetic (EM) pulses and receiving backscattered (reflected from different subsurface targets) echoes. The echoes are coherently represented to generate radargrams. A radargram is used to characterize subsurface target structures. Interestingly, radargram signals depict sequential structures due to linearly homo-geneous subsurface target features such as ice layers. Several automatic techniques are proposed to characterize the subsurface targets in the radargrams mostly associated with the probabilistic models or CNN-based deep learning models. The CNN-based architectures explicitly model the local spatial high dimensional contexts which are often infeasible for establishing the long-range sequential contextual relationship between local spatial features. Motivated by the aforementioned fact, we propose a hybrid CNN-Transformer-based encoder-decoder architectural framework for addressing the long-range sequential contextual dependencies within the sequential structures of RS signals. We tested the architecture on Multi-channel Coherent Radar Depth Sounder (MCoRDS) dataset. Experimental results confirm the capability of Transformers to characterize the subsurface targets.

Deep learning-based unsupervised change detection (CD) methods compare a prechange and a postchange image in deep feature space and require precise knowledge of the event date for selecting proper pre-/post-change images. However, in many applications changes may occur gradually over a span of time making pre-/post-dates difficult to establish or prior knowledge of event date is unknown. On the other hand, deep learning-based time-series analysis methods are generally supervised. Considering such scenarios, we propose a novel unsupervised deep learning-based method to detect changes in an image time-series. The method does not make any assumption on the date of the occurrence of the change event. It treats CD as an anomaly detection problem by exploiting multilayer long short term memory (LSTM) network to learn a representation of the time series. The proposed method ingests a shuffled time series and uses an encoder–decoder LSTM model to rearrange the input sequence in correct order. While the model fails to rearrange the changed pixels, unchanged data can be rearranged in the correct order. This enables the identification of the changed pixels. To show the effectiveness of the proposed method, we tested it on two multitemporal Sentinel-1 data sets over Brumadinho, Brazil, and Bhavanisagar, India.

The use of deep learning (DL) methods for change detection (CD) is currently dominated by supervised models that require a large number of labeled samples. However, these samples are difficult to acquire in the multitemporal case. A possible alternative is leveraging methods that exploit transfer learning for CD by reusing DL models pretrained for other tasks. However, the performance of the transfer-learning-based models decreases as much as the target images differ from the ones used for training the model. To overcome this limit, we propose an unsupervised CD method that exploits multiresolution deep feature maps derived by a convolutional autoencoder (CAE). It automatically learns spatial features from the input during the training phase without requiring any labeled data. The proposed method processes the bitemporal images to obtain and compare multiresolution bitemporal feature maps. These feature maps are then analyzed by a feature-selection technique to select the most discriminant ones. Furthermore, an aggregated multiresolution difference image is computed and used for a detail-preserving multiscale CD. In the context of this CD approach, we propose two alternative strategies to retrieve multiscale reliability maps. We tested the proposed method on bitemporal multispectral images acquired by Landsat-5 and Landsat-8 representing burned areas and Sentinel-2 images representing deforested areas. Results confirm the effectiveness of the proposed CD technique.

During the last decades, radar sounders provided direct measurements (radargrams) of the Earth’s polar caps’ subsurface. Radargrams are of critical importance for a better understanding of glaciologic structures and processes of the ice sheet in the framework of climate change. This article aims to automatically extract information on basal boundary conditions given their substantial relevance for modeling the ice-sheet processes, such as the sliding. We introduce a novel automatic method based on deep learning to detect the basal layer and basal units in radargrams acquired in the inland of icy areas. Radargrams are segmented into englacial layers, bedrock, basal units, and noise-limited regions; the latter includes the echo-free zone (EFZ), thermal noise, and signal perturbation. The network is a U-Net with attention gates and the Atrous Spatial Pyramid Pooling (ASPP) module that automatically extract semantically meaningful features at different scales. Experimental results on two datasets acquired in north Greenland and west Antarctica by the Multichannel Coherent Radar Depth Sounder (MCoRDS3) indicate a high overall segmentation accuracy. The accuracy of basal ice and signal perturbation detection is high, and that of the other classes is comparable with the literature techniques based on handcrafted features. The results show the effectiveness of the proposed method in automatically extracting semantically meaningful features to segment radargrams and map the basal layer and basal units.

Lava tubes are buried channels that transport thermally insulated lava. Nowadays, lava tubes on the Moon are believed to be empty and thus indicated as potential habitats for humankind. In recent years, several studies investigated possible lava tube locations, considering the gravity anomaly distribution and surficial volcanic features. This article proposes a novel and unsupervised method to map candidate buried empty lava tubes in radar sounder data (radargrams) and extract their physical properties. The approach relies on a model that describes the geometrical and electromagnetic (EM) properties of lava tubes in radargrams. According to this model, reflections in radargrams are automatically detected and analyzed with a fuzzy system to identify those associated with lava tube boundaries and reject the others. The fuzzy rules consider the EM and geometrical properties of lava tubes, and thus, their appearance in radargrams. The proposed method can address the complex task of identifying candidate lava tubes on a large number of radargrams in an automatic, fast, and objective way. The final decision on candidate lava tubes should be taken in postprocessing by expert planetologists. The proposed method is tested on both a real and a simulated data set of radargrams acquired on the Moon by the Lunar Radar Sounder (LRS). Identified candidate lava tubes are processed to extract geometrical parameters, such as the depth and the thickness of the crust (roof).

Radar Sounders (RSs) are nadir-looking sensors operating in high frequency (HF) or very high frequency (VHF) bands that profile subsurface targets to retrieve miscellaneous scientific information. Due to complex electromagnetic interaction between back-scattered returns, the interpretation of RS data is challenging. The investigations of ice-sheet subsurface structures require automatic techniques to account for both the sequential spatial distribution of subsurface targets and relevant statistical properties embedded in RS signals. Automatic techniques exist for characterizing these targets either related to probabilistic inference models or convolutional neural network (CNN) deep learning methods. Unfortunately, CNN-based methods capture local spatial context and merely model the global spatial context. In contrast to CNN, the Transformer-based models are reliable architectures for capturing long-range sequence-to-sequence global spatial contextual prior. Motivated by the aforementioned fact, we propose a novel Transformer-based semantic segmentation architecture named TransSounder to effectively encode the sequential structures of the RS signals. The TransSounder was constructed on a hybrid TransUNet-TransFuse architectural framework to systematically augment the modules from TransUNet and TransFuse architectures. Experimental results obtained using the Multichannel Coherent Radar Depth Sounder (MCoRDS) dataset confirms the robustness and capability of Transformers to accurately characterize the different subsurface targets.

Change detection is a long-standing and challenging problem in remote sensing. Very often, features about changes are difficult to model beforehand, thus making the collection of changed samples a challenging task. In comparison, it is much easier to collect numerous no-change samples. It is possible to define a change detection approach by using only easily available annotated no-change samples, which we henceforth call one-class change detection. Autoencoder networks being trained on no-change data are natural candidates for addressing this task due to their superior performance as compared to other one-class classification models. However, the autoencoders usually suffer from the problem of overgeneralization, i.e., they tend to generalize too well, thus risking properly reconstructing changed samples. In this paper, we propose a novel data-enclosing-ball minimizing autoencoder (DebM-AE) that is trained with dual objectives—a reconstruction error criterion and a minimum volume criterion. The network learns a compact latent space, where encodings of no-change samples have low intra-class variance, which as counter part has the identification of changed instances. We conducted extensive experiments on three real-world data sets. Results demonstrate advantages of the proposed method over other competitors. We make our data and code publicly available1.

Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both. It reduces redundancy among spectral bands while trying to preserve the original information of the image. By now, many efforts have been made to develop unsupervised band selection approaches, of which the majorities are heuristic algorithms devised by trial and error. In this article, we are interested in training an intelligent agent that, given a hyperspectral image, is capable of automatically learning policy to select an optimal band subset without any hand-engineered reasoning. To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning. Once the agent is trained, it learns a band-selection policy that guides the agent to sequentially select bands by fully exploiting the hyperspectral image and previously picked bands. Furthermore, we propose two different reward schemes for the environment simulation of deep reinforcement learning and compare them in experiments. This, to the best of our knowledge, is the first study that explores a deep reinforcement learning model for hyperspectral image analysis, thus opening a new door for future research and showcasing the great potential of deep reinforcement learning in remote sensing applications. Extensive experiments are carried out on four hyperspectral data sets, and experimental results demonstrate the effectiveness of the proposed method. The code is publicly available.

Sparse unmixing (SU) has been widely investigated for hyperspectral analysis with the aim to find the optimal subset of spectral signatures in a spectral library (known in advance) that can optimally model each pixel of the given hyperspectral image. Usually, the available spectral library organizes spectral signatures in groups. However, most existing strategies do not take full advantage of the inherent properties in the library. In this article, we design a convex framework for SU that incorporates the group structure of the spectral library. The convex framework includes two kinds of algorithms derived from either the primal or the dual form of the alternating direction method of multipliers (ADMM). Then, the convergence properties of the convex framework are established. Based on the convex framework, a novel nonconvex framework is developed for unmixing, which provides a new manner to enhance the sparsity of solution. The core of the nonconvex framework is to design a nonconvex penalty function for efficient minimization utilizing the generalized shrinkage mapping. The penalty function can be regarded as a closer approximation of the$l_{0}$norm. Experiments conducted on simulated and real hyperspectral data demonstrate the superiority and effectiveness of the proposed nonconvex framework in improving the unmixing performance and enhancing the sparsity of solution with respect to state-of-the-art techniques.

A satellite mission onboard a radar sounder for the observation of the earth’s polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.

The current remote sensing (RS) open data policy for multispectral (MS) missions such as Sentinel-2 and Landsat-8, together with the availability of free cloud distributed processing platforms such as Google Earth Engine, makes it possible the quick generation of burned area (BA) products even for nonexperts in the field. Indeed, fires and BAs can be detected using burn severity indices, which are usually obtained by simple band algebra operations. However, simple approaches can aid BA estimation only if typical error patterns are known and accounted for, especially when working at large (e.g., continental) scales. This article proposes an automatic BA detection system based on burn severity index thresholding, which integrates dedicated false and missed alarm mitigation strategies to improve the detection accuracy. The system is tested on Sentinel-2 and Landsat-8 data over ten different locations in Europe and spanning year 2018. Three known burn severity indices plus a custom one defined to improve the performance in the considered study area are under study. Results show that burned index thresholding is possible within accuracy bounds slightly larger than the state of the art, which is acceptable by considering the proposed simplified processing framework.

Deep Learning (DL) methods are widely used for Change Detection (CD) in multi-temporal Remote Sensing (RS) images. The recently reported unsupervised DL CD methods alleviate the problem of the labeled data collection affecting the supervised ones. Many of them exploit the DL models (e.g., Convolutional Autoencoder (CAE)) as a feature extractor and use the retrieved features to detect the changes. However, these features do not efficiently preserve the geometrical details, and they do not optimize the selection of informative features for change detection. We propose an unsupervised DL CD method that exploits the features extracted by a CAE trained with a super-resolution based loss function. The loss function allows the CAE to be trained to reconstruct the spatial information thus generating features preserving the geometrical details. The proposed method exploits a feature selection based on the Structured Similarity Index (SSIM) to perform a texture analysis and chooses couples of bi-temporal features providing relevant information about changes. We tested the proposed method on a couple of bi-temporal Landsat-8 images representing a burned area near Granada, Spain.

This paper presents the SaTellite RAdar sounder for earTh sUbsurface Sensing (STRATUS), which is a satellite mission for Earth Observation (EO) with an onboard instrument capable of probing the Earth's subsurface in polar and arid regions. STRATUS is based on an innovative distributed radar sounder (RS) with the unique capability to obtain continuous and large-scale subsurface measurements, with homogeneous and consistent quality in two of the least characterized and crucial frontiers of Earth: globally on the polar ice sheets, i.e., Greenland and Antarctica (primary objective), and regionally on the arid areas and deserts. STRATUS is a ground-breaking exploratory mission addressing crucial scientific questions. It provides new fundamental data that have not been acquired by any other past or present remote sensing mission on the Earth, with an expected high and genuine scientific return enabling the assessment of the climate change signature in the Earth subsurface.

Radar sounder data are widely used for investigating geological structures and processes in the subsurface of icy and arid areas. Visual interpretation is one of the main techniques used in the literature to extract information from radargrams. There exist some automatic approaches but mostly supervised. However, no methods exploit deep learning in an unsupervised way. Here, we propose an automatic and unsupervised technique for extracting information on the subsurface geological targets. The technique is built upon three steps: i) generation of a coarse segmentation map based on the radargram statistical properties, ii) refinement of the coarse map with deep learning to detect target reflections, and iii) analysis of the deep features to identify buried targets. We tested the proposed method on MARSIS radar data acquired near the South Pole of Mars. The experimental results prove the effectiveness of the proposed method.

Radar sounders (RSs) provide information on the subsurface of the cryosphere through the use of electromagnetic (EM) signals by producing radargrams. Radargrams are used to detect and analyze relevant targets in the subsurface of icy regions. Up to now, studies of the subsurface structure of the cryosphere with radargrams have been conducted manually or applying semiautomatic techniques. However, these techniques present efficiency and adaptability disadvantages. To overcome these issues, we propose automatic analysis techniques for radargrams of icy regions based on deep learning (DL). Experimental analysis is conducted for the automatic segmentation of areas of interest in radargrams of ice shelves of coastal areas acquired by the radar sounder MCoRDS2.

Recent satellite missions have initiated a new era in the area of Satellite Image Time Series (SITS) analysis by providing a huge number of High Resolution (HR) spectral-temporal images. The availability of HR images opens a door to an unprecedented wide range of possibilities to produce and develop high resolution Land Cover (LC) and Land Cover Change (LCC) maps. The goal of this paper is to effectively use high spatio-temporal resolution images to generate LCC maps by defining a novel automatic and unsupervised deep learning method based on three-dimensional (3D) Convolutional Neural Network (CNN). The method extracts spatio-temporal information from long SITS by using a pre-trained 3D CNN, detects changes and locates them in space and time. Experiments have provided promising results over both Amazonia and Saudi Arabia in the period 2013–2017, and has been compared to the other well-known LCC detection method.

With the remarkable development of spectral unmixing, the sparse-representation-based approaches have emerged as a promising alternative. The sparse-representation-based approaches aim at finding the optimal subset of a spectral library that can optimally model each pixel of a given hyperspectral image in a semisupervised fashion. The classic sparse unmixing models are solved by the prime alternating direction method of multipliers (pADMMs). However, the computation task of pADMM is heavy and time consuming. In this letter, we design a novel dual-alternating direction method of multipliers (dADMMs) for the classic sparse unmixing models. We also present the global convergence analysis of our algorithm in some special cases. As shown in our experiments, the proposed algorithm is more effective than the state-of-the-art algorithms.

To overcome the limited capability of most state-of-the-art change detection (CD) methods in modeling spatial context of multispectral high spatial resolution (HR) images and exploiting all spectral bands jointly, this letter presents a novel unsupervised deep-learning-based CD method that can effectively model contextual information and handle the large number of bands in multispectral HR images. This is achieved by exploiting all spectral bands after grouping them into spectral-dedicated band groups. To eliminate the necessity of multitemporal training data, the proposed method exploits a data set targeted for image classification to train spectral-dedicated Auxiliary Classifier Generative Adversarial Networks (ACGANs). They are used to obtain pixelwise deep change hypervector from multitemporal images. Each feature in deep change hypervector is analyzed based on the magnitude to identify changed pixels. An ensemble decision fusion strategy is used to combine change information from different features. Experimental results on the urban, Alpine, and agricultural Sentinel-2 data sets confirm the effectiveness of the proposed method.

Most change detection (CD) methods are unsupervised as collecting substantial multitemporal training data is challenging. Unsupervised CD methods are driven by heuristics and lack the capability to learn from data. However, in many real-world applications, it is possible to collect a small amount of labeled data scattered across the analyzed scene. Such a few scattered labeled samples in the pool of unlabeled samples can be effectively handled by graph convolutional network (GCN) that has recently shown good performance in semisupervised single-date analysis, to improve change detection performance. Based on this, we propose a semisupervised CD method that encodes multitemporal images as a graph via multiscale parcel segmentation that effectively captures the spatial and spectral aspects of the multitemporal images. The graph is further processed through GCN to learn a multitemporal model. Information from the labeled parcels is propagated to the unlabeled ones over training iterations. By exploiting the homogeneity of the parcels, the model is used to infer the label at a pixel level. To show the effectiveness of the proposed method, we tested it on a multitemporal Very High spatial Resolution (VHR) data set acquired by Pleiades sensor over Trento, Italy.

Crown features derived from high-density airborne laser scanning (ALS) data have proven to be effective for forest species classification at the individual tree level. Most of the general state-of-the-art (SoA) techniques rely on coarse-level crown features extracted from ALS data and under-utilize both the spatial and the spectral information available in the point clouds, Moreover, they are designed on the expected properties of the specific analyzed forest. We present a novel species classification approach, based on quantization of the entire 3-D tree crown into smaller elementary crown volumes (ECVs) that effectively captures the spatial distribution of filled (i.e., stem, branch, and foliage) and empty volumes of crowns. In the first step, a data-driven process dynamically tests and compares three quantization strategies to tailor the definition of the ECV to the forest type (e.g., conifer and deciduous forest). In the second step, for each ECV, a histogram vector is made up of features representing the light detection and ranging (LiDAR) point distribution and intensity to model the internal and the external local crown characteristics. Then, tree histogram feature vectors are obtained by stacking all the ECV histogram feature vectors. Finally, classification is performed by a support vector machine (SVM) classifier using the histogram intersection kernel. All experiments were performed on three high-density (50-200 points/m2) ALS data sets of deciduous, conifer, and mixed (i.e., both deciduous and conifer) trees. The higher classification accuracy of the proposed method over the SoA one proves its ability to better capture the crown characteristics of individual trees, including species-specific traits.

Building change detection (CD), important for its application in urban monitoring, can be performed in near real time by comparing prechange and postchange very-high-spatial-resolution (VHR) synthetic-aperture-radar (SAR) images. However, multitemporal VHR SAR images are complex as they show high spatial correlation, prone to shadows, and show an inhomogeneous signature. Spatial context needs to be taken into account to effectively detect a change in such images. Recently, convolutional-neural-network (CNN)-based transfer learning techniques have shown strong performance for CD in VHR multispectral images. However, its direct use for SAR CD is impeded by the absence of labeled SAR data and, thus, pretrained networks. To overcome this, we exploit the availability of paired unlabeled SAR and optical images to train for the suboptimal task of transcoding SAR images into optical images using a cycle-consistent generative adversarial network (CycleGAN). The CycleGAN consists of two generator networks: one for transcoding SAR images into the optical image domain and the other for projecting optical images into the SAR image domain. After unsupervised training, the generator transcoding SAR images into optical ones is used as a bitemporal deep feature extractor to extract optical-like features from bitemporal SAR images. Thus, deep change vector analysis (DCVA) and fuzzy rules can be applied to identify changed buildings (new/destroyed). We validate our method on two data sets made up of pairs of bitemporal VHR SAR images on the city of L'Aquila (Italy) and Trento (Italy).

This paper presents the Subsurface Radar Sounder (SRS) instrument onboard European Space Agency's (ESA) EnVision mission. EnVision is one of the three candidates selected for the Cosmic Vision 2015-2025 M5 medium-class missions. It is aimed at exploring the activity, the geologic history and the atmosphere of Venus. SRS is an orbital ground-penetrating radar with the unique science objectives of understanding the evolution of Venus' surface by searching for subsurface dielectric interfaces in the top hundreds of metres of the crust. In the paper, we describe the main science objectives of SRS, the performance evaluation under expected target conditions, the instrument design and the acquisition strategy that maximize the scientific returns.

This paper presents an approach for large-scale precise mapping of agricultural fields based on the analysis of Satellite Image Time Series (SITS) acquired by ESA Sentinel-2 (S2) satellite constellation. The approach has been developed in the framework of the ESA SEOM - Scientific Exploitation of Operational Missions - S2-4Sci Land and Water project. The goal is to design a flexible and automatic processing chain able to perform mapping in massive data. Here we focus on precision agriculture products generation at country level. In particular, the Country of study is Italy and the application goal is precision agriculture of single crop fields. To achieve this goal, two macro challenges are considered: (i) download and pre-processing of S2 SITS, and (ii) multi-temporal (MT) fine characterization of agricultural fields. Both challenges are addressed in an automatic way by exploiting and/or updating state-of-the-art methodologies. Promising results have been obtained over years 2017 and 2018 for Italy.

Very-high-resolution (VHR) multi-temporal images are important in remote sensing to monitor the dynamics of the Earth surface. Image semantic segmentation classifies pixels and assigns them label from meaningful object groups. It has been extensively studied in context of single image analysis, however not explored for multi-temporal one. In this paper we propose to extend supervised semantic segmentation to the unsupervised joint segmentation of multi-temporal images. The proposed method processes multi-temporal images by separately feeding them to a deep network comprising of trainable convolutional layers. The training process does not involve any external label. Segmentation labels are obtained from argmax classification of the final layer. Multi-temporal segmentation labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on a VHR dataset from Trento, Italy. Both quantitative and qualitative results demonstrated the effectiveness of the proposed approach.

Information from polarimetric synthetic aperture radar (PolSAR) imagery has been used for detecting built-up targets in classification problems, whereas it has been poorly exploited for change detection in multitemporal images. In this letter, we proposed an unsupervised approach for the detection of built-up changed areas from multitemporal full-polSAR images. The approach is based on the automatic thresholding of a novel change index based on the joint use of polarimetric span and average-alpha multitemporal information. The index is proposed for highlighting both constructed and demolished built-up elements. The experimental results on multitemporal UAVSAR images demonstrate that the proposed approach provides high detection accuracy and effectively separates among different types of changes, which is not the case with standard methods.

Satellite image time series (SITS), such as those by Sentinel-2 (S2) satellites, provides a large amount of information due to their combined temporal, spatial, and spectral resolutions. The high revisit frequency and spatial resolution of S2 result in: 1) increase in the probability of acquiring cloud-free images and 2) availability of detailed information for analyzing small objects. These characteristics are of interest in precision agriculture, where temporally dense SITS can benefit the understanding of crop behaviors. In the past, information about agricultural practices has been collected over large regions and focused on mixed/aggregated crops due to the poor tradeoff between the spatial and temporal resolutions. Products have been generated at low spatial resolution and daily basis or at high spatial resolution and weekly/monthly basis. They are meaningful for large agricultural fields, whereas they are limited when fields show a small average size. In this context, S2 characteristics allow for both high spatial and temporal resolution products. However, no existing automatic method effectively separates small fields from each other in an unsupervised way and deals with data irregularly sampled in time. Thus, this article presents a method suitable for the analysis of small crop fields in S2 dense SITS that accounts for S2 characteristics. The method fuses spatio-temporal information, analyzes data spatio-temporal evolution, and extracts relevant spatio-temporal information. The effectiveness of the proposed method was corroborated by experiments carried out on S2-SITS acquired over an area located in Barrax, Spain.

The estimation of a snow-covered area (SCA) is often achieved by classification of imagery acquired by passive optical sensors aboard satellite platforms with high revisit frequencies [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)] required by various applications. The extraction of the SCA from optical imagery is inevitably hindered by the presence of clouds, where the surface labels (i.e., snow and no-snow) remain unknown. In the bulk of existing research, cloud pixels are either masked out without any further processing or assigned to snow/no-snow classes by performing spatial or temporal filters. The current approaches to deal with the cloud-obscuration problem are subjected to sizable uncertainties. They mostly neglect or only partially account for the temporal correlation, which undermines the full potential of long time series. We propose a novel method for estimating snow/no-snow labels beneath the clouds that leverages the multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, the date of acquisition (and thus the season), and the cloud-obscuration duration. The proposed method is built upon analyzing the long time series of maps derived from the single date classification of images in order to estimate the conditional probabilities of transition between the snow and no-snow classes. The probabilities are estimated as a function of the aforementioned environmental and multitemporal factors, which allow for the prediction of labels beneath the clouds in either archive or new acquisitions. Validation results on a four-year time series of daily MODIS images acquired over the Euregio region in Italian and Austrian Alps prove the effectiveness and robustness of the proposed method in assigning labels beneath the clouds.

Change detection (CD) is a crucial topic in many remote sensing applications. In the recent years, satellite polarimetric synthetic aperture radar (PolSAR) systems (e.g., the Sentinel-1 constellation) became a suitable tool for multitemporal monitoring due to the regular acquisitions with a short revisit time in different polarimetric channels. Methods for CD in PolSAR data mainly focus on binary CD (i.e., they provide information about the presence/absence of change only), whereas the polarimetric enhanced information provides multiple features that can be exploited for performing multiclass CD. In this article, we introduce a novel framework for the characterization of multitemporal changes in dual-polarimetric data. The framework is based on the definition of polarimetric change vectors (PCVs) and their representation in a polar coordinate system. PCVs allow characterizing and, thus, to separate multiclass changes in terms of target properties of the single-time scenes and the scattering theory. The proposed model is used to: 1) derive the statistical behaviors of change and no change classes in PolSAR multitemporal images; 2) design an automatic and unsupervised strategy to estimate the optimal number of changes; and 3) distinguish no change from change classes and the kinds of change from each other. An experimental analysis has been conducted on three multitemporal PolSAR data sets having different complexities in terms of number and kinds of change classes. The results confirm the effectiveness of the proposed approach and the better performance with respect to both specific techniques for CD in dual-pol SAR data and a general multiclass CD method, not designed for PolSAR data.

High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth's surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento.

Crop-type classification has been attracting a lot of attention in recent years. In particular since the launch of the Sentinel-2 (S2) satellite which combines a large amount of spectral and spatial information, compared to previous satellite generations. In the literature, several methods exist that perform crop classification in time series, but most of them: i) work at pixel level; ii) perform single-data analysis; and/or iii) consider a single feature. This results in low performance of state-of-the-art methods. This paper presents an approach that works at object-level and exploits both spatial and temporal information coded in NDVI time series and phenological parameters and takes advantage of a semi-supervised paradigm by combining a new hierarchical correlation clustering with an artificial neural network. The effectiveness of the proposed approach was corroborated over an intensive cultivated area located in Barrax, Spain. Crop-type classification was compared to state-of-the-art methods.

Radar sounders (RS) can be used to acquire data on ice sheets and provide direct evidence of the structures in the subsurface. Many acquisitions are available from airborne RS in the Antarctica and Greenland. However, airborne data are costly, have limited spatial coverage, and nonhomogeneous characteristics. To overcome these limitations, a potential satellite-mounted RS could provide uniform coverage and consistent data quality at the cost of lower resolution and higher path loss. In this paper, we assess the performance of a possible Earth-orbiting RS by simulating and analyzing its radargrams. The simulation approach reprocesses existing airborne RS to match the orbital RS characteristics. The simulated radargrams are analyzed to estimate the losses and understand the detection performance of icy targets using state-of-the-art data analysis techniques. The preliminary analysis of the simulated radargrams indicates that, under the simplified assumptions, an orbiting RS will be capable of imaging the investigated subsurface targets.

Accurate three dimensional (3D) stem modeling is critical to individual tree analysis. Multi-scan Terrestrial Laser Scanning (TLS) systems accurately map fine 3D structural details of tree components including the stem, branches and leaves. State-of-the-art (SoA) methods to model stem are affected by problems such as occlusion and point density variation around the stem. Thus, we propose a voxel based approach to derive accurate 3D model of stem by exploiting the opacity-driven (to lasers) void volume formed within the stem. For each height slice, peaks detected in the external boundary of the point-density-map object generated jointly from points derived from the stem model and the proximal TLS data points correspond to branch-knots. All experiments were conducted on a set of 10 manually delineated trees belonging to pine and spruce. The preliminary results prove the effectiveness of the method to accurately model the 3D stem and localize branch-knots.

This paper introduces a novel method for estimation of snow/no-snow labels for cloud-obscured pixels in order to enable an accurate mapping of the snow-covered area (SCA) in time series. The proposed method leverages the embedded information in multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, date of acquisition, and the cloud obscuration duration. The proposed method is built upon three main steps: i) classification of single date images into three classes (snow, no-snow, and cloud), ii) estimation of conditional probabilities of class-transition in relation with the environmental factors, and iii) prediction of the snow/no-snow labels for the cloud-obscured pixels. We validated the proposed method on daily MODIS images acquired over 10 years in a mountain area located in Italy and Austria. The proposed method yielded SCA improved maps compared to a standard method of assigning labels beneath the clouds.

Change Detection (CD) using multitemporal satellite images is an important application of remote sensing. In this work, we propose a Convolutional-Neural-Network (CNN) based unsupervised multiple-change detection approach that simultaneously accounts for the high spatial correlation among pixels in Very High spatial Resolution (VHR) images and the differences in multisensor images. We accomplish this by learning in an unsupervised way a transcoding between multisensor multitemporal data by exploiting a cycle-consistent Generative Adversarial Network (CycleGAN) that consists of two generator CNN networks. After unsupervised training, one generator of the CycleGAN is used to mitigate multisensor differences, while the other is used as a feature extractor that enables the computation of multitemporal deep features. These features are then compared pixelwise to generate a change detection map. Changed pixels are then further analyzed based on multitemporal deep features for identifying different kind of changes (multiple-change detection). Results obtained on multisensor multitemporal dataset consisting of Quickbird and Pleiades images confirm the effectiveness of the proposed approach.

One of the most common approaches to unsupervised change detection (CD) in multispectral images is change vector analysis (CVA). CVA computes the multispectral difference image and exploits its statistical distribution in (hyper-) spherical coordinates by means of two steps: 1) magnitude and 2) direction thresholding. The two steps require assumptions on: 1) the model of class distributions and 2) the number of changes. However, both assumptions are seldom satisfied or difficult to formulate, especially when considering VHR images. Thus, we propose an approach to multiple CD in VHR optical images based on iterative clustering and adaptive thresholding in (hyper-) spherical coordinate. The proposed approach: 1) is distribution free; 2) is unsupervised; 3) automatically identifies the number of changes; and 4) is robust to noise. Results obtained on two multitemporal single-sensor and multisensor data sets, including images from WorldView-2 and QuickBird, corroborate the effectiveness of the proposed approach.

Spaceborne radar sounders are nadir-looking sensors operating in the high frequency (HF) or very high frequency (VHF) bands with subsurface sensing capabilities. Due to technological limitations, this type of sensors often deploys omnidirectional antennas. This results in undesired artifacts such as off-nadir clutter which could hinder data interpretation. Very recent technological advancements open up the possibility of synthesizing very large antenna apertures in HF/VHF band by using small satellites array deployed in suitable orbital formation flying. Accordingly, in this study, we propose a novel concept of distributed radar sounder system. The proposed concept is complemented with a mathematical model for performance prediction which takes into account the uncertainty on the position of the sensors. Moreover, we discuss possible orbital solutions for the problem of the deployment of the distributed radar sounder system. The results show that a distributed radar sounder operating in small satellites formation flying is particularly appealing as it can: 1) substantially reduce the impact of surface clutter; 2) increase the across-track resolution; 3) increase the signal-to-noise ratio (SNR) (or, alternatively, decrease the overall required transmitted power with respect to a traditional single configuration radar sounder design); and 4) provide large flexibility in the data processing of the signals acquired by the different sensors.

The availability of multitemporal images acquired by several very high geometrical resolution (VHR) optical sensors makes it possible to build VHR image time series (TS) of images acquired over the same geographical area with a temporal resolution better than the one achievable when considering a single VHR sensor. However, such TS include images showing different characteristics from the geometrical, radiometrical, and spectral viewpoint. Thus, there is a need of methods for building homogeneous VHR optical TS when using multispectral multisensor images. By focusing on the spectral domain, we propose a method to transform a VHR image into the spectral domain of another image in the same multisensor TS but acquired by a different sensor. To this end, a prediction-based approach relying on a nonparametric regression method is employed to mitigate sensor-dependent spectral differences. The impact of possible changes occurred on the ground is mitigated by training the prediction model on unchanged samples, only. Experimental results obtained on VHR optical multisensor images confirm the effectiveness of the proposed approach.

Accurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both the dominant and subdominant tree crowns in conifer-dominated multistoried forests using small footprint high-density airborne Light Detection and Ranging data. Here, 3-D candidate cloud segments delineated using a canopy height model segmentation technique are projected onto a novel 3-D space where both the dominant and subdominant tree crowns can be accurately detected and delineated. Tree crowns are detected using 2-D features derived from the projected data. The delineation of the crown is performed at the voxel level with the help of both the 2-D features and 3-D texture information derived from the cloud segment. The texture information is modeled by using 3-D Gray Level Co-occurrence Matrix. The performance evaluation was done on a set of six circular plots for which reference data are available. The high detection and delineation accuracies obtained over the state of the art prove the performance of the proposed method.

The empirical (regression-based) methods for estimation of water quality parameters are mostly built upon the features derived from the original feature space of optical imagery (e.g., band ratios). This article aims at examining novel features to retrieve in-water constituents including chlorophyll-a (Chl-a), total suspended solids (TSS), and colored dissolved organic matter (CDOM). In this article, direction cosines and transformation of either color space or the coordinate system are applied to the original feature space in order to derive new features. A full-search approach is exploited to identify the optimal band combination for a given type of feature. The proposed analysis seeks for a band combination among all the possible ones that yield the strongest correlation through regressing a given feature against the concentration of the constituent of interest. The effectiveness of the proposed features is examined against standard ones using radiative transfer simulations, in situ measurements, and satellite imagery in a wide range of in-water optical conditions. The simulated and in situ data enabled in-depth analyses on the efficacy of recent satellite sensors with the primary focus of the aquatic science community [Operational Land Imager (OLI), MutiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI)] for retrieval of in-water constituents. TSS and Chl-a concentration of two alpine lakes (Lake Constance and Lake Lucerne) are also mapped using a real OLI image. The results suggest the effectiveness of the proposed features that can be leveraged to estimate the constituents in inland/coastal waters. OLI-based retrievals of in-water constituents proved difficulties in optically complex waters, whereas enhanced spectral resolution of MSI and OLCI permitted accurate estimates.

Hyperspectral (HS) images provide a dense sampling of target spectral signatures. Thus, they can be used in a multitemporal framework to detect and discriminate between different kinds of fine spectral change effectively. However, due to the complexity of the problem and the limited amount of multitemporal images and reference data, only a few works in the literature addressed change detection (CD) in HS images. In this paper, we present a novel method for unsupervised multiple CD in multitemporal HS images based on a discrete representation of the change information. Differently from the state-of-the-art methods, which address the high dimensionality of the data using band reduction or selection techniques, in this paper, we focus our attention on the representation and exploitation of the change information present in each band. After a band-by-band pixel-based subtraction of the multitemporal images, we define the hyperspectral change vectors (HCVs). The change information in the HCVs is then simplified. To this end, the radiometric information of each band is separately analyzed to generate a quantized discrete representation of the HCVs. This discrete representation is explored by considering the hierarchical nature of the changes in HS images. A tree representation is defined and used to discriminate between different kinds of change. The proposed method has been tested on a simulated data set and two real multitemporal data sets acquired by the Hyperion sensor over agricultural areas. Experimental results confirm that the discrete representation of the change information is effective when used for unsupervised CD in multitemporal HS data.

Change detection (CD) in multitemporal images is an important application of remote sensing. Recent technological evolution provided very high spatial resolution (VHR) multitemporal optical satellite images showing high spatial correlation among pixels and requiring an effective modeling of spatial context to accurately capture change information. Here, we propose a novel unsupervised context-sensitive framework-deep change vector analysis (DCVA)-for CD in multitemporal VHR images that exploit convolutional neural network (CNN) features. To have an unsupervised system, DCVA starts from a suboptimal pretrained multilayered CNN for obtaining deep features that can model spatial relationship among neighboring pixels and thus complex objects. An automatic feature selection strategy is employed layerwise to select features emphasizing both high and low prior probability change information. Selected features from multiple layers are combined into a deep feature hypervector providing a multiscale scene representation. The use of the same pretrained CNN for semantic segmentation of single images enables us to obtain coherent multitemporal deep feature hypervectors that can be compared pixelwise to obtain deep change vectors that also model spatial context information. Deep change vectors are analyzed based on their magnitude to identify changed pixels. Then, deep change vectors corresponding to identified changed pixels are binarized to obtain a compressed binary deep change vectors that preserve information about the direction (kind) of change. Changed pixels are analyzed for multiple CD based on the binary features, thus implicitly using the spatial information. Experimental results on multitemporal data sets of Worldview-2, Pleiades, and Quickbird images confirm the effectiveness of the proposed method.

While image matching has been studied in remote sensing community for decades, matching multimodal data [e.g., optical, light detection and ranging (LiDAR), synthetic aperture radar (SAR), and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, we present a novel fast and robust template matching framework integrating local descriptors for multimodal images. First, a local descriptor [such as histogram of oriented gradient (HOG) and local self-similarity (LSS) or speeded-up robust feature (SURF)] is extracted at each pixel to form a pixelwise feature representation of an image. Then, we define a fast similarity measure based on the feature representation using the fast Fourier transform (FFT) in the frequency domain. A template matching strategy is employed to detect correspondences between images. In this procedure, we also propose a novel pixelwise feature representation using orientated gradients of images, which is named channel features of orientated gradients (CFOG). This novel feature is an extension of the pixelwise HOG descriptor with superior performance in image matching and computational efficiency. The major advantages of the proposed matching framework include: 1) structural similarity representation using the pixelwise feature description and 2) high computational efficiency due to the use of FFT. The proposed matching framework has been evaluated using many different types of multimodal images, and the results demonstrate its superior matching performance with respect to the state-of-the-art methods.

This paper presents a multitemporal technique for multi-class Change Detection (CD) between pairs of images of a satellite image time series. Changes between different pair of images within a time series must be consistent with each other since images acquired over the same scene are causally related with one another. The temporal consistency of the pixel status can be used to formulate a principle that constrains the CD results within the series to be mutually consistent. This principle coincides with the conservative property of the change variable and it allows the unsupervised validation of changes detected between arbitrary image pairs. Thus, all images in the series, rather than a single couple, are used in the pair-wise CD. The proposed technique was applied to a dataset of dual-polarized terrain-corrected SAR images acquired by Sentinel-1. Experimental results show the validity of the proposed multitemporal approach in improving the CD results.

Satellite Image Time Series (SITS), such as the ones acquired by the new Sentinel-2 (S2), combine a large amount of information compared to previous satellite generations since a better trade-off in terms of spatial/spectral/temporal resolutions is guaranteed. The specific characteristic of acquiring images under overlapped orbits, offered by S2, results in: i) availability of irregularly sampled acquisitions and ii) increase of the probability to acquire cloud free images over time. This characteristic becomes relevant in the agricultural analysis, where availability of dense SITS is required to map and analyze fast working crop behaviors. In the literature, several methods exist that extract phenological parameters for agricultural analysis, but none of them is able to deal with irregularly sampled data. Thus, this paper presents an approach for derivation of cropland phenological parameters from irregularly sampled S2-SITS. Experimental results obtained on S2-SITS acquired over Barrax, Spain, confirm the effectiveness of the proposed approach.

Lunar lava tubes are buried channels that contained thermally insulated lava during the volcanic period of the Moon. Nowadays, they are believed to be empty and thus, identified as potential habitats for humans. In recent years, numerous studies investigated the possible locations of these tubes by taking into account the distribution of gravity anomalies and the volcanic features of the surface. In this paper, we model lava tubes according to their electromagnetic behavior, and we propose a novel approach to locate lava tubes and estimate their physical properties. The method analyzes the subsurface reflections stored in radargrams to extract the desired features automatically. Then, these features and their relationships are processed by a fuzzy rule-based system to detect the presence or absence of lava tubes. The strategy was implemented and successfully tested on simulated radargrams with various surface properties and tunnel dimensions.

The knowledge on the species of individual trees is ineluctable for accurate forest parameter estimation and related studies. Terrestrial Laser Scanning (TLS) remote sensing systems acquire a huge number of point samples that contain very accurate and detailed three dimensional (3D) information of tree structures. Every tree species has unique internal and external crown structural characteristics that can be modeled from its TLS data. However, methods in the state of the art show reduced performance due to inaccurate modeling of tree structures such as the crown, and the branch, and poor selection of features. The proposed method leverages on the fine internal and external crown structural information in TLS data to achieve species classification. We remove noise and stem points in TLS data using a novel voxel neighborhood density-based technique. Internal and external crown geometric features derived from the branch level, and the crown level, respectively, are provided to a non linear Support Vector Machines (SVM) to achieve species classification, and evaluate feature relevance. All experiments were conducted on a set of 75 manually delineated trees belonging to the Spruce, the Pine, and the Birch species.

Change Detection (CD) using multi-temporal satellite images is a fundamental application of remote sensing. To effectively capture change information from Very High spatial Resolution (VHR) optical images, spatial context needs to be modelled as VHR images are characterized by high spatial correlation among pixels. We propose a context-sensitive framework for CD in multitemporal VHR images using pre-trained Convolutional-Neural-Network (CNN)-based feature extraction. Such a framework, while unsupervised, can effectively model the spatial relationship among neighbouring pixels in VHR images. A CNN, pre-trained for semantic segmentation, enables us to obtain multi-temporal deep features that are compared pixelwise to identify changed pixels. Changed pixels are further clustered for multiple change detection. Results obtained on multi-temporal datasets of Worldview-2 and Pleiades images demonstrate effectiveness of our approach.

Spaceborne low-frequency and wide bandwidth radar sounders are a promising technology to regularly investigate at global-scale Earth's icy and arid regions. However, Earth ionosphere distorts the radar signal impacting performance parameters, such as subsurface resolution, of the radar system. One of the most relevant distortions that a sounder signal in the lower part of the very high-frequency (VHF) band (e.g., 40-50 MHz) encounters is the distortion of the phase component that could become mission critical if not properly compensated. Low-frequency and high fractional bandwidth radar systems are particularly affected by this issue. Previous works on radar sounder ionosphere phase distortion compensation addressed the Martian ionosphere and used techniques based on the Taylor series expansion. In this letter, we focus on the Earth ionosphere and we exploit a recently proposed ionosphere compensation technique based on the Legendre orthogonal polynomials expansion, which proved to be more accurate than the compensation based on Taylor expansion. Simulations show that the method allows a nominal compensation of the phase distortions under realistic ionosphere scenarios expected during the acquisitions. Furthermore, it proved to be accurate and robust for total electron content conditions expected during nighttime for all the geomagnetic latitudes. The results confirm that the method can accurately compensate the distorting effects on the phase component of a spaceborne VHF radar sounder.

Change detection (CD) between a pair of images is a popular problem in remote sensing. Despite a large amount of data is acquired every day by remote sensing satellites, standard CD methods usually consider only the two target images between which we desire to detect changes. The aim of this work is to present a novel framework in which the bi-temporal CD is redefined by evaluating the consistency of the changes occurred in the target image pair with all the other changes of images within the considered time series. Our approach evaluates pixel-wise the changes in temporal closed-loops that include the two target images where the resulting binary change/no-change sequences can be processed by strategies inspired to the error-control-coding theory. Unreliable CD results for the target images can be identified and corrected. The experimental results on both a synthetic and a real dataset demonstrate the effectiveness of the proposed framework.

Urban change detection is an important part of monitoring operations and disaster relief efforts. However, often sufficient ground truth data is not available to use traditional supervised machine learning techniques. In this paper, a novel Deep Learning based weakly-supervised framework for urban change detection using multi-temporal polarimetric SAR data is proposed. A modified unsupervised stacked auto-encoder stage is used to learn an efficient representation of the multi-temporal polarimetric information. Then a label aggregation is performed in the feature space before classification by a multi-layer perceptron. The proposed methodology is validated on a L-band UAVSAR dataset acquired over Los Angeles, CA and performs accurately and effectively with a low false alarm rate.

Airborne Light Detection and Ranging (LIDAR) remote sensing based forest inventory at the individual tree level is a valuable and effective alternative to manual inventory, due to factors such as higher accuracy, easy repeatability of sampling, and economic benefits. However, individual tree detection in multi-storied forests is challenging due to high tree proximity and forest structure complexity issues. In this work, we aim at detecting subdominant trees in a multi-stored forest from high density small foot-print multi-return airborne LiDAR data. The marker controlled watershed segmentation is used for the three dimensional (3D) delineation of the dominant tree crowns. The data associated with every segment are separately projected onto a novel 3D space, where crown surface information is effectively represented and subdominant trees are highlighted. A set of ten features is employed to separate subdominant from dominant trees. Preliminary results prove the effectiveness of the proposed method.

A novel spectral-spatial joint multiscale approach is developed to address the multi-class change detection problem in bitemporal multispectral remote sensing images. The proposed approach is based on a multiscale morphological compressed change vector analysis (M2C2VA), which extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) while preserving more geometrical details of change targets. In particular, spectral change features are reconstructed according to the morphological analysis which exploiting the interaction of a pixel with its adjacent regions. Two multiscale ensemble strategies are proposed to integrate the change information represented at multiple scales in order to enhance the CD performance. The proposed approach is designed in an unsupervised fashion thus can be implemented without using ground reference data. A pair of real bitemporal remote sensing images is used to test the proposed approach and the obtained experimental results confirm its effectiveness.

Multitemporal Hyperspectral (HS) images can be used in Change Detection (CD) to identify and discriminate among different kinds of change due to the fine sampling of the spectrum by HS sensors. In this work we propose a novel method for unsupervised multiple CD in multitemporal HS data based on binary Spectral Change Vectors (SCVs) and an agglomerative hierarchical clustering. First, we perform binary CD to separate changed from unchanged pixels. Second, we convert the real valued SCVs into binary ones. Thus we move from a real valued high dimensional space to a discrete one. The binary signatures are used to construct a dendrogram following an hierarchical agglomerative clustering approach. Finally, we exploit the hierarchical structure to discriminate among the kinds of change in a fully unsupervised manner. The experimental results obtained on the real dataset confirmed the effectiveness of the proposed method.

Change detection in large urban areas is an application with increasing relevance. In this domain, Polarimetric SAR (PolSAR) sensors are receiving more attention recently. The enhanced polarimetric information provides useful features which can describe multi-temporal changes. In this work, we aim at introducing an approach for unsupervised change detection with focus on built-up areas that relies on the polarimetric information. This approach is based on the analysis of the multi-temporal α feature obtained from the Cloude-Pottier eigenvalue/eigenvector decomposition. Large differences in the α values can be associated to changes in the dominant scattering mechanism. These are likely to be associated to buildings when built-up areas are considered. Changes are detected according to an automatic and unsupervised approach. Validation is conducted on a pair of UAVSAR images acquired over Los Angeles, USA. Preliminary results highlight the effectiveness of proposed approach.

In this paper, a segmentation-based approach to fine registration of multispectral and multitemporal very high resolution (VHR) images is proposed. The proposed approach aims at estimating and correcting the residual local misalignment [also referred to as registration noise (RN)] that often affects multitemporal VHR images even after standard registration. The method extracts automatically a set of object representative points associated with regions with homogeneous spectral properties (i.e., objects in the scene). Such points result to be distributed all over the considered scene and account for the high spatial correlation of pixels in VHR images. Then, it estimates the amount and direction of residual local misalignment for each object representative point by exploiting residual local misalignment properties in a multiple displacement analysis framework. To this end, a multiscale differential analysis of the multispectral difference image is employed for modeling the statistical distribution of pixels affected by residual misalignment (i.e., RN pixels) and detect them. The RN is used to perform a segmentation-based fine registration based on both temporal and spatial correlation. Accordingly, the method is particularly suitable to be used for images with a large number of border regions like VHR images of urban scenes. Experimental results obtained on both simulated and real multitemporal VHR images confirm the effectiveness of the proposed method.

The knowledge of the tree species is a crucial information that governs the success of precision forest management practice. High-density small footprint multireturn airborne light detection and ranging (LiDAR) scanning can collect a huge amount of point samples containing structural details of the forest vertical profile, which can reveal important structural information of the forest components. LiDAR data have been successfully used to distinguish between coniferous and deciduous/broadleaved tree species. However, species classification within a class (e.g., the conifer class) using LiDAR data is a challenging problem when considering the tree external crown characteristics only. This paper presents a novel method for conifer species classification based on the use of geometric features describing both the internal and external structures of the crown. The internal crown geometric features (IGFs) are defined based on a novel internal branch structure model, which uses 3-D region growing and principal component analysis to delineate the branch structure of a conifer tree accurately. IGFs are used together with external crown geometric features to perform conifer species classification. Three different support vector machines have been considered for classification performance evaluation. The experimental analysis conducted on high-density LiDAR data acquired over a portion of the Trentino region in Italy proves the effectiveness of the proposed method.

When dealing with optical images, the most common approach to unsupervised change detection is Change Vector Analysis (CVA) which computes the multispectral difference image and exploits its statistical distribution in (hyper-)spherical coordinates. The latter step usually requires assumptions on both the model of class distributions and the number of changes. However, both assumptions are seldom satisfied especially when multisensor VHR images are considered. Thus, we propose an approach to multiple change detection in multisensor VHR optical images based on iterative clustering in (hyper-) spherical coordinate. The proposed approach is distribution free, unsupervised and automatically identifies the number of changes. Results obtained on a multitemporal and multisensor dataset including images from WorldView-2 and QuickBird are promising.

The knowledge about the species of trees is essential for precision forest management practices. Modern high density airborne Light Detection and Ranging (LiDAR) systems have the ability to acquire large number of LiDAR points, allowing a very detailed characterization of the forest at the individual tree level. In this context, it is possible to use LiDAR data for accurate classification of the tree species. In this paper, we consider the specific problem of species classification of trees belonging to the conifer class. This is particularly challenging when only the external geometric information is considered. To address the problem we propose a novel approach that model the internal crown structure of the conifers. The internal structure is identified by using 3D region growing and Principal Component Analysis (PCA) and is used for defining a set of novel Internal Crown Geometric features (IGFs). Some state-of-the-art External Crown Geometric Features (EGFs) were also used to improve the classification accuracy. Sparse Support Vector Machines (SSVM) was used for classification and to quantify the feature relevances.

Even after coregistration, very high resolution (VHR) multitemporal images acquired by different multispectral sensors (e.g., QuickBird and WordView) show a residual misregistration due to dissimilarities in acquisition conditions and in sensor properties. Residual misregistration can be considered as a source of noise and is referred to as registration noise (RN). Since RN is likely to have a negative impact on multitemporal information extraction, detecting and reducing it can increase multitemporal image processing accuracy. In this letter, we propose an approach to identify RN between VHR multitemporal and multisensor images. Under the assumption that dominant RN mainly exists along boundaries of objects, we propose to use edge information in high frequency regions to estimate it. This choice makes RN detection less dependent on radiometric differences and thus more effective in VHR multisensor image processing. In order to validate the effectiveness of the proposed approach, multitemporal multisensor data sets are built including QuickBird and WorldView VHR images. Both qualitative and quantitative assessments demonstrate the effectiveness of the proposed RN identification approach compared to the state-of-the-art one.

This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral-temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method.

Given a user-defined image time series (i.e., the query time series), content based time series retrieval (CBTSR) is the process of identifying other time series that show properties similar to the query. When dealing with time series, the elements of the content based retrieval process require to be redefined in order to take into account the time variable. In this perspective, the design of the query, the feature extraction, and retrieval itself have to be reformulated. Here we focus our attention to CBTSR in pairs of images. The goal is to identify bi-temporal images showing a specific kind of change (associated with changes on the ground) modeled by the query. Attention is devoted to the design of the auxiliary archive modeling the change information and on the retrieval algorithm. Experiments on an archive of Landsat images confirmed the effectiveness of the proposed approach.

This paper presents the Radar for Icy Moon Exploration (RIME) that is a fundamental payload in the Jupiter Icy Moon Explorer (JUICE) mission of the European Space Agency (ESA). RIME is a radar sounder aimed to study the subsurface of Jupiter's icy moons Ganymede, Europa and Callisto. The paper illustrates the main goals of RIME, its architecture and parameters and some recent advances in its design.

The availability of multitemporal images acquired by several very high geometrical resolution (VHR) optical sensors makes it possible to build VHR image Time-Series (TS) with a temporal resolution better than the one achievable when considering a single sensor. However, such TS include images showing different characteristics from the geometrical, radiometrical and spectral viewpoint. Thus, there is a need of methods for building consistent VHR optical TS when using multispectral Multi-Sensor (MS) images. Here we focus on the spectral domain only, by designing a method to transform one image in an MS-TS into the spectral domain of another image in the same MS-TS, but acquired by a different sensor. To this end, a prediction-based approach relying on Artificial Neural Networks (ANN) is employed. In order to mitigate the impacts of possible changes occurred on the ground, the prediction model estimation is based on unchanged samples only. Experimental results obtained on VHR optical MS images confirm the effectiveness of the proposed approach.

Very High Resolution (VHR) multitemporal images show a residual misalignment even after applying effective state of the art co-registration. This residual misalignment is caused by the dissimilarities of the acquisition circumstances such as off-nadir angle of the sensor, stability of the acquisition platform, structure of the considered scene, and so on. This paper aims at mitigating the residual misalignment of VHR multitemporal images to get a fine co-registration result. Here we propose to use Registration Noise (RN), which represents misaligned samples, for refining co-registration. After standard co-registration, a local analysis of RN pixels is fulfilled for extracting Control Points (CPs) and matching them according to the amount of the RN pixels. Matched CPs are employed for generating a deformation map to warp one image to the other image. Experiments carried out on both simulated and real multitemporal VHR images acquired by QuickBird sensors confirm the validity of the analysis and effectiveness of the proposed method.

This paper develops a novel multitemporal spectral unmixing (MSU) approach for addressing the challenging multiple-change detection problem in bi-temporal hyperspectral (HS) images. Differently from state-of-the-art techniques that mainly perform at a pixel level, the proposed MSU approach investigates the spectral-temporal variations at a subpixel level. A multitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted and employed for distinguishing change and no-change MT-EMs in the unmixing model. The CD problem is solved by analyzing the abundances of the unique change and no-change multitemporal endmembers and their contribution to each pixel. Experimental results obtained on multitemporal Hyperion HS images confirmed the effectiveness of the proposed method.

This letter addresses the problem of unsupervised land-cover classification of remotely sensed multispectral satellite images from the perspective of cluster ensembles and self-learning. The cluster ensembles combine multiple data partitions generated by different clustering algorithms into a single robust solution. A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier, although trained without any external supervision, reduces the effect of data overlapping from different clusters which otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual clustering of the ensemble.

This paper addresses the problem of land-cover classification of remotely sensed image pairs in the context of domain adaptation. The primary assumption of the proposed method is that the training data are available only for one of the images (source domain), whereas for the other image (target domain), no labeled data are available. No assumption is made here on the number and the statistical properties of the land-cover classes that, in turn, may vary from one domain to the other. The only constraint is that at least one land-cover class is shared by the two domains. Under these assumptions, a novel graph theoretic cross-domain cluster mapping algorithm is proposed to detect efficiently the set of land-cover classes which are common to both domains as well as the additional or missing classes in the target domain image. An interdomain graph is introduced, which contains all of the class information of both images, and subsequently, an efficient subgraph-matching algorithm is proposed to highlight the changes between them. The proposed cluster mapping algorithm initially clusters the target domain data into an optimal number of groups given the available source domain training samples. To this end, a method based on information theory and a kernel-based clustering algorithm is proposed. Considering the fact that the spectral signature of land-cover classes may overlap significantly, a postprocessing step is applied to refine the classification map produced by the clustering algorithm. Two multispectral data sets with medium and very high geometrical resolution and one hyperspectral data set are considered to evaluate the robustness of the proposed technique. Two of the data sets consist of multitemporal image pairs, while the remaining one contains images of spatially disjoint geographical areas. The experiments confirm the effectiveness of the proposed framework in different complex scenarios.

Even after applying effective coregistration methods, multitemporal images are likely to show a residual misalignment, which is referred to as registration noise (RN). This is because coregistration methods from the literature cannot fully handle the local dissimilarities induced by differences in the acquisition conditions (e.g., the stability of the acquisition platform, the off-nadir angle of the sensor, the structure of the considered scene, etc.). This paper addresses the problem of reducing such a residual misalignment by proposing a fine automatic coregistration approach for very high resolution (VHR) multispectral images. The proposed method takes advantage of the properties of the residual misalignment itself. To this end, RN is first extracted in the change vector analysis (CVA) polar domain according to the behaviors of the specific multitemporal images considered. Then, a local analysis of RN pixels (i.e., those showing residual misalignment) is conducted for automatically extracting control points (CPs) and matching them according to their estimated displacement. Matched CPs are used for generating a deformation map by interpolation. Finally, one VHR image is warped to the coordinates of the other through a deformation map. Experiments carried out on simulated and real multitemporal VHR images confirm the effectiveness of the proposed approach.

The new generation of satellite hyperspectral (HS) sensors can acquire very detailed spectral information directly related to land surface materials. Thus, when multitemporal images are considered, they allow us to detect many potential changes in land covers. This paper addresses the change-detection (CD) problem in multitemporal HS remote sensing images, analyzing the complexity of this task. A novel hierarchical CD approach is proposed, which is aimed at identifying all the possible change classes present between the considered images. In greater detail, in order to formalize the CD problem in HS images, an analysis of the concept of “change” is given from the perspective of pixel spectral behaviors. The proposed novel hierarchical scheme is developed by considering spectral change information to identify the change classes having discriminable spectral behaviors. Due to the fact that, in real applications, reference samples are often not available, the proposed approach is designed in an unsupervised way. Experimental results obtained on both simulated and real multitemporal HS images demonstrate the effectiveness of the proposed CD method.

This paper presents an effective semiautomatic method for discovering and detecting multiple changes (i.e., different kinds of changes) in multitemporal hyperspectral (HS) images. Differently from the state-of-the-art techniques, the proposed method is designed to be sensitive to the small spectral variations that can be identified in HS images but usually are not detectable in multispectral images. The method is based on the proposed sequential spectral change vector analysis, which exploits an iterative hierarchical scheme that at each iteration discovers and identifies a subset of changes. The approach is interactive and semiautomatic and allows one to study in detail the structure of changes hidden in the variations of the spectral signatures according to a top-down procedure. A novel 2-D adaptive spectral change vector representation (ASCVR) is proposed to visualize the changes. At each level this representation is optimized by an automatic definition of a reference vector that emphasizes the discrimination of changes. Finally, an interactive manual change identification is applied for extracting changes in the ASCVR domain. The proposed approach has been tested on three hyperspectral data sets, including both simulated and real multitemporal images showing multiple-change detection problems. Experimental results confirmed the effectiveness of the proposed method.

The increasing availability of very high resolution (VHR) images regularly acquired over urban areas opens new attractive opportunities for monitoring human settlements at the level of individual buildings. This paper presents a novel approach to building change detection in multitemporal VHR synthetic aperture radar (SAR) images. The proposed approach is based on two concepts: 1) the extraction of information on changes associated with increase and decrease of backscattering at the optimal building scale and 2) the exploitation of the expected backscattering properties of buildings to detect either new or fully demolished buildings. Each detected change is associated with a grade of reliability. The approach is validated on the following: 1) COSMO-SkyMed multitemporal spotlight images acquired in 2009 on the city of L'Aquila (Italy) before and after the earthquake that hit the region and 2) TerraSAR-X multitemporal spotlight images acquired on the urban area of the city of Trento (Italy). Results demonstrate that the proposed approach allows an accurate identification of new and demolished buildings while presents a low false-alarm rate and a high reliability.

In this paper, we present a novel domain adaptation technique aimed at providing reliable change detection maps for a series of image pairs acquired on the same area at different times. The proposed technique exploits the polar change vector analysis method and assumes that the reference data for characterizing a specific change of interest are available only for a pair of images (source domain). Then, it exploits the knowledge learned from the source domain and adapts it to other pairs of images belonging to the time series (target domains) to be analyzed. The proposed technique is able to handle possible radiometric differences among images adapting in an unsupervised way the decision rule estimated on the source domain to the target domains through variables estimated directly on the target images. The proposed approach has been applied to two data sets made up of time series of Landsat Thematic Mapper images. In one case, the change of interest is related to evolution of deforestation, while in the other case, it is related to burned area detection. Experimental results show the effectiveness of the proposed technique.

The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches.

This paper presents a novel approach to binary change detection in pairs of images extracted from time series. The main idea is that, given a binary change detection map obtained with any literature technique applied to the considered pair of images, we can identify possible change detection errors exploiting other images in the time series. This can be done by considering other pairs of images in the time series that, jointly with the analyzed one, can define a closed circular path in time. Then we model the binary change variable as a conservative field along circular paths within the time series. If for a pixel the circular path does not satisfy the conservativeness property an error is detected. Accordingly, the change detection label on that pixel is considered unreliable Experimental results obatined on a time series of ASAR Envisat images point out the effectiveness of the approach in detecting unreliable pixels.

This paper focuses on a challenging task for representing and detecting multiple changes in multitemporal hyperspectral images. To this aim, a novel Sequential Spectral Change Vector Analysis (S2CVA) method is proposed that extends the use of the popular C2VA method [1]. The proposed S2CVA approach is designed in a sequential and semiautomatic fashion, where a fully automatic 2-D change representation and an interactive change identification are included at each level of the processing, exploiting the multiple change information hierarchically. In particular, an adaptive reference vector scheme is developed to drive the change representation, and thus the sequential analysis, by following a top-down structure. Changes are represented and separated according to their spectral change significance. Experimental results obtained on multitemporal Hyperion images confirm the effectiveness of the proposed method.

In this paper an approach that exploits and combines the acquisition modes offered by satellite SAR systems is presented that: i) quickly and automatically identifies the areas severely affected by a catastrophic event (i.e., hotspots), such as an earthquake by analyzing images characterized by a large coverage and a medium to high geometrical resolution; and ii) analyzes images characterized by very high geometrical resolution acquired over hot-spots in order to detect collapsed buildings. Experimental results obtained on a dataset made up of COSMO-SkyMed (CSK) data acquired before and after the 2009 L'Aquila earthquake (Italy) demonstrate the effectiveness of the proposed approach.

This paper proposes a novel neural model for unsupervised change detection in time series of multispectral remote sensing imagery using clustering with Self-Organizing Map (SOM) for automatic pseudo-training sample set selection cascaded with Concurrent Self-Organizing Maps (CSOM) classifier. The proposed algorithm has the following steps: (a) computation of difference image (DI) corresponding to the magnitudes of Spectral Change Vectors (SCVs); (b) SOM clustering to automatically deduce the SCV domain quantization parameters defining the pseudo-training sample set regions (changed, unchanged and uncertain); (c) CSOM classification. The model is evaluated using a Landsat-5 image set acquired on a Mexico area before and after two wildfires. As a benchmark, we have considered the classical method of Bayes theory-EM algorithm for selection of pseudo-training sample set combined with a S3VM classifier. The results confirm the effectiveness of our neural approach. Moreover, the exciting advantage of the proposed model over the classical ones is that it does not require any statistical assumptions regarding changed/unchanged SCVs data and it implies a reduced computational effort.

This paper presents an adaptive framework for detection of changes of relevance occurring in image time series in a recursive way. With the availability of reference data for only one image pair from the time series (source domain), the proposed methodology employs change vector analysis in the 3-dimensional spherical domain to determine a decision region R associated with the change of relevance. Then, by exploiting the similarity among domains, the same kind of change can be detected by adapting R to the rest of image pairs belonging to the time series. The methodology was tested in a multispectral time series made up by TM-Landsat images marked by sequential deforestation activities in the Amazon with reference data. The quantitative analysis of the results indicates the soundness of the proposed approach.

In this manuscript introduces a novel 4 dimensional tool for visualizing and analyzing long time series. The proposed tool is thought to be a support for an intuitive visual analysis of long time series and a starting point for the definition of novel methods for automatic image information mining in time series. The proposed tool is based on the mathematical smooth space curve known as helix. This choice aims at preserving the time neighborhood of acquisitions as a space neighborhood in the 4D representation (e.g., the proximity of subsequent days over the New Year). The impact and usefulness of the proposed tool is validated on a long time series of MODIS Terra Surface Reflectance Daily L2G Global (MOD09GA) products with a spatial resolution of 500m acquired from 1stJanuary 2005 to 31stDecember 2012 for a total of 2845images.

This paper presents the Radar for Icy Moons Exploration (RIME) instrument, which has been selected as payload for the JUpiter Icy moons Explorer (JUICE) mission. JUICE is the first Large-class mission chosen as part of the ESA's Cosmic Vision 2015-2025 programme, and is aimed to study Jupiter and to investigate the potentially habitable zones in the Galilean icy satellites. RIME is a radar sounder optimized for the penetration of Ganymede, Europa and Callisto up to a depth of 9 km in order to allow the study of the subsurface geology and geophysics of the icy moons and detect possible subsurface water. In this paper we present the main science goals of RIME, the main technical challenges for its development and for its operations, as well as the expected scientific returns.

This paper presents a novel sequential cascade classification technique for automatically updating land-cover maps by classifying remote sensing image time series. We assume that a reliable training set is initially available only for one of the images (i.e., the source domain) in the time series, whereas it is not for an image being classified (i.e., the target domain). Unlike the standard cascade classification method, the proposed method aims at exploiting all the images in the time series acquired between the target and source domains to effectively classify the target domain. To this end, initially `pseudo' training sets of the images are defined by a multiple pairwise change detection based transfer learning strategy. Then, the target domain is classified by the proposed sequential cascade classification method, exploiting the temporal correlation between images. Experimental results obtained on a time series of Landsat multispectral images show the effectiveness of the proposed technique with respect to the standard cascade classification.

This paper addressed the change-detection problem in multitemporal hyperspectral remote sensing images (CD-HS). The concept of “change” in multitemporal hyperspectral images is analyzed from the viewpoint of single pixel spectral signal. A novel hierarchical change-detection approach is proposed by considering both the change magnitude and spectral change information, which aims to identify the change classes having discriminable spectral behaviors. The proposed method is developed in an unsupervised way thus to provide a solution for real CD-HS cases, for which reference samples are often not available. Experimental results obtained on multitemporal Hyperion hyperspectral images confirm the effectiveness of the proposed change-detection approach.

This paper presents a novel approach to multitemporal detection of primitives in very high resolution (VHR) SAR images. The proposed approach aims at exploiting the monotemporal as well as the multitemporal information in order to both: i) identify transitions in the state of primitives (i.e., detect changes); and ii) improve the monotemporal detection of primitives taking explicitly advantage of the temporal correlation. The performance of the multitemporal detector is evaluated on a time series of four TerraSAR-X images acquired over the city of Lüneburg in Germany. Experimental results confirm the effectiveness of the proposed approach.

This paper presents an approach to the detection of changed buildings using multitemporal Very High Resolution (VHR) Synthetic Aperture Radar (SAR) images. The proposed approach is based on two concepts i) the extraction of information on changes associated with increase and decrease of backscattering at the optimal building scale; and ii) the exploitation of the expected backscattering proprieties of buildings to detect new and fully destroyed buildings with their grade of reliability. Experimental results obtained on a dataset made up of two COSMO-SkyMed (CSK©) spotlight images acquired in 2009 over the city of L'Aquila (Italy) before and after an earthquake demonstrated that the proposed approach allows an effective identification of destroyed buildings.

A new approach to change detection in very high resolution remote sensing images based on morphological attribute profiles (APs) is presented. A multiresolution contextual transformation performed by APs allows the extraction of geometrical features related to the structures within the scene at different scales. The temporal changes are detected by comparing the geometrical features extracted from the image of each date. The experiments performed on panchromatic QuickBird images related to an urban area show the effectiveness of the proposed technique in detecting changes on the basis of the spatial morphology by preserving geometrical detail.

This paper addresses change detection in multitemporal remote sensing images. After a review of the main techniques developed in remote sensing for the analysis of multitemporal data, the attention is focused on the challenging problem of change detection in very-high-resolution (VHR) multispectral images. In this context, we propose a framework that aims at defining a top-down approach to the design of the architecture of novel change-detection systems for multitemporal VHR images. The proposed framework explicitly models the presence of different radiometric changes on the basis of the properties of multitemporal images, extracts the semantic meaning of radiometric changes, identifies changes of interest with strategies designed on the basis of the specific application, and takes advantage of the intrinsic multiscale/multilevel properties of the objects and the high spatial correlation between pixels in a neighborhood. This framework defines guidelines for the development of a new generation of change-detection methods that can properly analyze multitemporal VHR images taking into account the intrinsic complexity associated with these data. In order to illustrate the use of the proposed framework, a real change-detection problem has been considered, which is described by a pair of VHR multispectral images acquired by the QuickBird satellite on the city of Trento, Italy. The proposed framework has been used for defining a system for change detection in the two images. Experimental results confirm the effectiveness of the developed system and the usefulness of the proposed framework.

The availability of very high resolution (VHR) synthetic aperture radar (SAR) images, which can be acquired by satellites over the same geographical area with short repetition interval, makes the development of effective unsupervised change detection (CD) techniques very important. This paper proposes a hierarchical approach to CD in VHR SAR images for addressing surveillance applications, where VHR data are acquired with high temporal resolution (e.g., one image every few days). The proposed approach is based on two concepts: exploitation of a multiscale technique for a preliminary detection of areas containing changes in backscattering at different scales (hot spots) and explicit modeling of the semantic meaning of changes by using both the intrinsic SAR image properties (e.g., acquisition geometry and scattering mechanisms) and the available prior information. In order to illustrate the effectiveness of the proposed approach, a problem of freight traffic surveillance is addressed considering two data sets. Each of them is made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) constellation in spotlight mode. Each data set defines a complex CD problem due to both the presence of a variety of changes on the ground and the complexity of object backscattering. Experimental results point out the effectiveness of the proposed approach.

This paper proposes a novel change-detection-driven transfer learning (TL) approach to update land-cover maps by classifying remote-sensing images acquired on the same area at different times (i.e., image time series). The proposed approach requires that a reliable training set is available only for one of the images (i.e., the source domain) in the time series whereas it is not for another image to be classified (i.e., the target domain). Unlike other literature TL methods, no additional assumptions on either the similarity between class distributions or the presence of the same set of land-cover classes in the two domains are required. The proposed method aims at defining a reliable training set for the target domain, taking advantage of the already available knowledge on the source domain. This is done by applying an unsupervised-change-detection method to target and source domains and transferring class labels of detected unchanged training samples from the source to the target domain to initialize the target-domain training set. The training set is then optimized by a properly defined novel active learning (AL) procedure. At the early iterations of AL, priority in labeling is given to samples detected as being changed, whereas in the remaining ones, the most informative samples are selected from changed and unchanged unlabeled samples. Finally, the target image is classified. Experimental results show that transferring the class labels from the source domain to the target domain provides a reliable initial training set and that the priority rule for AL results in a fast convergence to the desired accuracy with respect to Standard AL.

Image classification usually requires the availability of reliable reference data collected for the considered image to train supervised classifiers. Unfortunately when time series of images are considered, this is seldom possible because of the costs associated with reference data collection. In most of the applications it is realistic to have reference data available for one or few images of a time series acquired on the area of interest. In this paper, we present a novel system for automatically classifying image time series that takes advantage of image(s) with an associated reference information (i.e., the source domain) to classify image(s) for which reference information is not available (i.e., the target domain). The proposed system exploits the already available knowledge on the source domain and, when possible, integrates it with a minimum amount of new labeled data for the target domain. In addition, it is able to handle possible significant differences between statistical distributions of the source and target domains. Here, the method is presented in the context of classification of remote sensing image time series, where ground reference data collection is a highly critical and demanding task. Experimental results show the effectiveness of the proposed technique. The method can work on multimodal (e.g., multispectral) images.

This paper proposes an approach to change detection in multitemporal very high geometrical resolution (VHR) SAR images for surveillance applications. The approach takes advantage of 3 concepts: i) multiscale representation for a preliminary detection of areas showing significant changes in backscattering between the two images (hot spots); ii) exploitation of prior information about typical usage of zones of interest in the area under control; and iii) definition of features and change detectors optimized for an effective detection of specific changes in each zone of interest. Here the proposed approach is designed for the solution of surveillance problems. A data set made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (CSK®) constellation in spotlight mode on the commercial port of Livorno (Italy) was used. Experimental results point out the effectiveness of the proposed approach.

This paper presents a novel system for automatically updating land-cover maps by classifying remote sensing image time series. The proposed system assumes that a reliable training set is available only for one of the images (i.e., the source domain) in the time series, whereas it is not for another image to be classified (i.e., the target domain). To effectively classify the target domain the proposed system includes two steps: i) low-cost definition of the training set for the target domain; and ii) target domain classification according to the Bayesian cascade decision rule that exploits the temporal correlation between domains. In the proposed system, the low cost training set for the target domain is defined on the basis of transfer and active learning methods, which also use the temporal dependence information between the domains. Experimental results obtained on a time series of Landsat multispectral images show the effectiveness of the proposed technique.

This paper presents a novel unsupervised target-driven change detection procedure for analyzing multi-temporal remote sensing images, which is based on data transformation and similarity measures. The iteratively reweighted multivariate alteration detection (IR-MAD) technique is firstly used to separate the various change information into MAD components. Then, the similarity measures are used to automatically search for the target-related component according to a pre-defined target-driven rule. This procedure both takes advantage of the IR-MAD transformation in change detection and helps users to quickly locate the transformed component associated with their interesting change target. Experimental results obtained on multitemporal Landsat ETM+ data confirm the effectiveness of the proposed approach.

When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. Moreover, the efficiency of the search is also an issue, since the samples labeled as background are not used by target detectors such as the support vector data description (SVDD). In this work we propose a competitive and effective approach to identify the most relevant training samples for one-class classification based on the use of an active learning strategy. The SVDD classifier is first trained with insufficient target examples. It is then used to detect the most informative samples to be labeled by a user through active learning techniques. By selecting unlabeled samples in a smart way and by adopting a diversity criterion, it is possible to obtain an accurate description of the class of interest with a relatively small number of training samples. The performance of the proposed method is illustrated in a change detection scenario and is validated by comparison with state-of-art active learning techniques originally developed for multiclass problems.

In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project “Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures,” funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.

This paper addresses the problem of land-cover map updating by classification of multitemporal remote-sensing images in the context of domain adaptation (DA). The basic assumptions behind the proposed approach are twofold. The first one is that training data (ground reference information) are available for one of the considered multitemporal acquisitions (source domain) whereas they are not for the other (target domain). The second one is that multitemporal acquisitions (i.e., target and source domains) may be characterized by different sets of classes. Unlike other approaches available in the literature, the proposed DA Bayesian classifier based on maximum a posteriori decision rule (DA-MAP) automatically identifies whether there exist differences between the set of classes in the target and source domains and properly handles these differences in the updating process. The proposed method was tested in different scenarios of increasing complexity related to multitemporal image classification. Experimental results on medium-resolution and very high resolution multitemporal remote-sensing data sets confirm the effectiveness and the reliability of the proposed DA-MAP classifier.

The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images havingBspectral bands are considered, an effective solution to this problem is to exploit all available spectral channels in the framework of supervised or partially supervised approaches. However, in many real applications, it is difficult/impossible to collect ground truth information for either multitemporal or single-date images. On the opposite, unsupervised methods available in the literature are not effective in handling the full information present in multispectral and multitemporal images. They usually consider a simplified subspace of the original feature space having small dimensionality and, thus, characterized by a possible loss of change information. In this paper, we present a framework for the detection of multiple changes in bitemporal and multispectral remote sensing images that allows one to overcome the limits of standard unsupervised methods. The framework is based on the following: 1) a compressed yet efficient 2-D representation of the change information and 2) a two-step automatic decision strategy. The effectiveness of the proposed approach has been tested on two bitemporal and multispectral data sets having different properties. Results obtained on both data sets confirm the effectiveness of the proposed approach.

This paper presents a novel iterative active learning (AL) technique aimed at defining effective multitemporal training sets to be used for the supervised detection of land-cover transitions in a pair of remote sensing images acquired on the same area at different times. The proposed AL technique is developed in the framework of the Bayes' rule for compound classification. At each iteration, it selects the pair of spatially aligned unlabeled pixels in the two images that are classified with the maximum uncertainty. These pixels are then labeled by an external supervisor and included in the training set. The uncertainty of a pair of pixels is assessed by the joint entropy defined by considering two possible different simplifying assumptions: 1) class-conditional independence and 2) temporal independence between multitemporal images. Accordingly, different algorithms are introduced. The proposed joint-entropy-based AL algorithms for compound classification are compared with each other and with a marginal-entropy-based AL technique (in which the entropy is computed separately on single-date images) applied to the postclassification comparison method. The experimental results obtained on two multispectral and multitemporal data sets show the effectiveness of the proposed technique.

In this paper an unsupervised approach to multiple change detection based on adaptive thresholding is proposed. The method is developed in the polar framework for change vector analysis in multispectral images recently presented in the literature. According to the properties of spectral change vectors in the polar framework a procedure is presented based on the following steps: i) identification of unchanged and changed pixels along the magnitude variable (different kinds of change are treated as being a single class); ii) isolation of different kinds of changes along the direction variable; iii) refinement of the decision threshold along the magnitude variable according to the properties of each kind of detected change considered independently. The proposed method is validated on a pair of multitemporal images acquired by the Landsat-5 satellite including 3 kinds of changes.

This paper addresses the problem of change detection (CD) in very high geometrical resolution (VHR) multitemporal images. In this context, we propose a conceptual framework that aims at defining: i) a taxonomy of different radiometric changes occurring when dealing with VHR remote sensing images; and ii) a global approach to the definition of the architecture of effective CD methods for VHR images. This framework defines precise guidelines for the development of a new generation of CD methods that can properly analyze multitemporal VHR images. The proposed framework is illustrated in the definition of a change detection method for the solution of a specific real problem related to the analysis of multitemporal QuickBird images.

This paper presents a novel active learning (AL) technique for the compound classification of multitemporal remote-sensing images for the detection of land-cover transitions. The proposed AL technique is based on the selection of unlabeled pairs of samples that have maximum uncertainty on their labels assigned by a classifier implemented according to the Bayes rule for compound classification. Uncertainty of a pair of samples is assessed by joint entropy defined on the basis of two different simplifying assumptions: i) class-conditional independence, and ii) temporal independence between multitemporal images. Accordingly, two algorithms for the proposed joint entropy based AL technique are introduced. The proposed joint entropy based AL algorithms are compared to each other and with a marginal entropy (entropy computed separately on single-date images) based AL technique. Experimental results obtained on two multispectral images show the effectiveness of the proposed technique.

In this paper a framework for the detection of multiple changes in multitemporal and multispectral remote sensing images is presented. The framework is based on: i) a compressed yet efficient (i.e., nearly lossless) 2-dimensional (2D) representation of the change information; and ii) a 2-step automatic decision strategy. At first, the original BD feature space to be explored for the solution of the change-detection (CD) problem is compressed to a 2D space in which the change information is clearly represented; then, the retrieved 2D space is explored for extracting in an automatic way the different kinds of change, thus generating the CD map. This procedure is conducted by applying a 2-step decision strategy based on the Bayes decision theory. Results obtained on a Landsat-5 and a QuickBird data sets confirm the effectiveness of the proposed approach in both representing the information in the 2D space and generating the CD map.

This paper addresses the problem of change detection (CD) in very high geometrical resolution (VHR) multitemporal images. In this context, we propose a general conceptual framework that aims at giving: i) a taxonomy of different radiometric changes occurring when dealing with VHR remote sensing images; and ii) a global approach to the definition of the architecture of effective CD methods for VHR images. This framework represents a first step for defining effective guidelines for the development of a new generation of CD methods that can be properly analyze multitemporal VHR images taking into account the intrinsic complexity associated with these data.

In this letter, we investigate the effects of pansharpening (PS) applied to multispectral (MS) multitemporal images in change-detection (CD) applications. Although CD maps computed from pansharpened data show an enhanced spatial resolution, they can suffer from errors due to artifacts induced by the fusion process. The rationale of our analysis consists in understanding to which extent such artifacts can affect spatially enhanced CD maps. To this end, a quantitative analysis is performed which is based on a novel strategy that exploits similarity measures to rank PS methods according to their impact on CD performance. Many multiresolution fusion algorithms are considered, and CD results obtained from original MS and from spatially enhanced data are compared.

This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. Inone-classimage classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one class are available, obtaining a reliable classifier is a difficult task. In the particular case of SVM-based classifiers, this task is even harder because the free parameters of the model need to be finely adjusted, but no clear criterion can be adopted. In order to improve the OC-SVM classifier accuracy and alleviate the problem of free-parameter selection, the information provided by unlabeled samples present in the scene can be used. In this paper, we present two state-of-the-art algorithms for semisupervised one-class classification for remote sensing classification problems. The first proposed algorithm is based on modifying the OC-SVM kernel by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples. The second one is based on a simple modification of the standard SVM cost function which penalizes more the errors made when classifying samples of the target class. The good performance of the proposed methods is illustrated in four challenging remote sensing image classification scenarios where the goal is to detect one of the classes present on the scene. In particular, we present results for multisource urban monitoring, hyperspectral crop detection, multispectral cloud screening, and change-detection problems. Experimental results show the suitability of the proposed techniques, particularly in cases with few or poorly representative labeled samples.

This paper presents a novel support vector machine classifier designed for sub-pixel image classification (pixel/spectral unmixing). The proposed classifier generalizes the properties of SVMs to the identification and modeling of the abundances of classes in mixed pixels by using fuzzy logic. This results in the definition of a fuzzy-input fuzzy-output support vector machine (F2SVM) classifier that can: i) process fuzzy information given as input to the classification algorithm for modeling the sub-pixel information in the learning phase of the classifier, and ii) provide a fuzzy modeling of the classification results, allowing a relation many-to-one between classes and pixels. The presented binary F2SVM can address multicategory problems according to two strategies: the fuzzy one-against-all (FOAA) and the fuzzy one-against-one strategies (FOAO). These strategies generalize to the fuzzy case techniques based on ensembles of binary classifiers used for addressing multicategory problems in crisp classification problems. The effectiveness of the proposed F2SVM classifier is tested on three problems related to image classification in presence of mixed pixels having different characteristics. Experimental results confirm the validity of the proposed sub-pixel classification method.

This paper presents an automatic context-sensitive technique robust to registration noise (RN) for change detection (CD) in multitemporal very high geometrical resolution (VHR) remote sensing images. Exploiting the properties of RN in VHR images, the proposed technique analyzes the distribution of the spectral change vectors (SCVs) computed according to the change vector analysis (CVA) in a quantized polar domain. The method studies the SCVs falling into each quantization cell at different resolution levels (scales) to automatically identify the effects of RN in the polar domain. This information is jointly exploited with the spatial context information contained in the neighborhood of each pixel for generating the final CD map. The spatial context information is modeled through the definition of adaptive regions homogeneous both in spatial and temporal domain (parcels). Experimental results obtained on real VHR remote sensing multitemporal images confirm the effectiveness of the proposed technique.

This paper presents a novel multiscale technique for unsupervised change detection in very high geometrical resolution images based on adaptive multiscale random fields (AMSRF). AMSRFs are defined according to hierarchical segmentation applied to multitemporal images. Under the assumption that the relationship between random fields at different scales can be modeled according to a Markov chain, the statistical distribution of classes is sequentially estimated from the finest to the coarsest scale, and class labels propagated from the coarsest to the finest one. The method is developed within the framework of the Bayes decision theory. Experimental results obtained on a SPOT-5 multitemporal data set confirm the effectiveness of the proposed approach.

This letter presents a novel parcel-based context-sensitive technique for unsupervised change detection in very high geometrical resolution images. In order to improve pixel-based change-detection performance, we propose to exploit the spatial-context information in the framework of a multilevel approach. The proposed technique models the scene (and hence changes) at different resolution levels defining multitemporal and multilevel ldquoparcelsrdquo (i.e., small homogeneous regions shared by both original images). Change detection is achieved by applying a multilevel change vector analysis to each pixel of the considered images. This technique properly analyzes the multilevel and multitemporal parcel-based context information of the considered spatial position. The adaptive nature of multitemporal parcels and their multilevel representation allow one a proper modeling of complex objects in the investigated scene as well as borders and details of the changed areas. Experimental results confirm the effectiveness of the proposed approach.

This paper analyzes the problem of change detection in very high resolution (VHR) multitemporal images by studying the effects of residual misregistration [registration noise (RN)] between images acquired on the same geographical area at different times. In particular, according to an experimental analysis driven from a theoretical study, the main effects of RN on VHR images are identified and some important properties are derived and described in a polar framework for change vector analysis. In addition, a technique for an adaptive and unsupervised explicit estimation of the RN distribution in the polar domain is proposed. This technique derives the RN distribution according to both a multiscale analysis of the distribution of spectral change vectors and the Parzen windows method. Experimental results obtained on simulated and real multitemporal data sets confirm the validity of the proposed analysis, the reliability of the derived properties on RN, and the effectiveness of the proposed estimation technique. This technique represents a very promising tool for the definition of change-detection methods for VHR multitemporal images robust to RN.

This paper presents a novel adaptive technique for change detection in very high geometrical resolution (VHR) Synthetic Aperture Radar (SAR) images that exploits information theoretical similarity measures for modeling the temporal evolution of probability density functions (pdfs). Image statistics for characterizing pdfs are adaptively estimated on a local basis by exploiting the spatial-context information of pixels on small homogeneous regions shared by multitemporal images (i.e., multitemporal "parcels"). The joint analysis of different orders statistics makes the method robust and suitable to the detection of both step changes of the backscattering and texture changes. The use of parcels allows one to model both complex objects in the investigated scene and borders of the changed areas and change details. Experimental results confirm the effectiveness of the proposed approach.

In this paper an automatic context-sensitive technique robust to registration noise (RN) for change detection on multitemporal very high geometrical resolution (VHR) images is presented. Exploiting the properties of RN in VHR images, the proposed technique analyzes the distribution of the spectral change vectors (SCVs) computed according to the change vector analysis (CVA) in a quantized polar domain. The method studies the SCVs falling into each quantization cell at different resolution levels (scales) to automatically identify the effects of RN in the polar domain. In order to improve the change-detection accuracy also the spatial-contextual information contained in the neighborhood of each pixel is considered through the definition of adaptive regions homogeneous both in spatial and temporal domain (parcels). The final change-detection map is generated considering both the information from the multiscale analysis and the spatial-contextual information. Experimental results obtained on real VHR multitemporal images confirm the effectiveness of the proposed approach.

An unsupervised technique for change detection (CD) in very high geometrical resolution images is proposed, which is based on the use of morphological filters. This technique integrates the nonlinear and adaptive properties of the morphological filters with a change vector analysis (CVA) procedure. Different morphological operators are analyzed and compared with respect to the CD problem. Alternating sequential filters by reconstruction proved to be the most effective, permitting the preservation of the geometrical information of the structures in the scene while filtering the homogeneous areas. Experimental results confirm the effectiveness of the proposed technique. It increases the accuracy of the CD process as compared with the standard CVA approach.

This letter presents a multistage clustering technique for unsupervised classification that is based on the following: 1) a graph-cut procedure to produce initial segments that are made up of pixels with similar spatial and spectral properties; 2) a fuzzy c-means algorithm to group these segments into a fixed number of classes; 3) a proper implementation of the expectation-maximization (EM) algorithm to estimate the statistical parameters of classes on the basis of the initial seeds that are achieved at convergence by the fuzzy c-means algorithm; and 4) the Bayes rule for minimum error to perform the final classification on the basis of the distributions that are estimated with the EM algorithm. Experimental results confirm the effectiveness of the proposed technique.

This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images in the original feature space without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudotraining set that is necessary for initializing an adequately defined binary semisupervised support vector machine classifier. Starting from these initial seeds, the performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. This algorithm models the full complexity of the change-detection problem, which is only partially represented from the seed pixels included in the pseudotraining set. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings. Experimental results obtained on different multispectral remote-sensing images confirm the effectiveness of the proposed approach.

This paper addresses the problem of matching the statistical properties of the distributions of two (or more) multi-spectral remote sensing images acquired on the same geographical area at different times. An N-D probability density function (pdf) matching technique for the preprocessing of multitemporal images is introduced in the remote sensing domain by defining and analyzing three important application scenarios: 1) supervised classification; 2) partially supervised classification; and 3) change detection. Unlike other methods adopted in remote sensing applications, the procedure considered performs the matching process by properly taking into account the correlation among spectral channels, thus retaining the data correlation structure after the pdf matching. Experimental results obtained on real multitemporal remote sensing data sets confirm the validity of the presented technique in all the considered scenarios.

This paper presents a novel approach to unsupervised change detection, which is based on the combined use of the change vector analysis (CVA) technique and the semisupervised support vector machine (S3VM) classification method. The proposed approach aims at analyzing the information present in multitemporal images by jointly analyzing their original spectral signatures. This is accomplished by using the CVA technique in a selective way for defining a pseudotraining set necessary for initializing the S3VM binary classifier. Then, starting from these initial seeds, the S3VM performs change detection in the original multitemporal feature space. This is done by gradually involving unlabeled multitemporal pixels in the semisupervised learning procedure for better modeling the decision boundary between changed and unchanged pixels. Experimental results obtained on different multispectral and multitemporal images confirm the effectiveness of the proposed approach.

This paper addresses unsupervised change detection by proposing a proper framework for a formal definition and a theoretical study of the change vector analysis (CVA) technique. This framework, which is based on the representation of the CVA in polar coordinates, aims at: 1) introducing a set of formal definitions in the polar domain (which are linked to the properties of the data) for a better general description (and thus understanding) of the information present in spectral change vectors; 2) analyzing from a theoretical point of view the distributions of changed and unchanged pixels in the polar domain (also according to possible simplifying assumptions); 3) driving the implementation of proper preprocessing procedures to be applied to multitemporal images on the basis of the results of the theoretical study on the distributions; and 4) defining a solid background for the development of advanced and accurate automatic change-detection algorithms in the polar domain. The findings derived from the theoretical analysis on the statistical models of classes have been validated on real multispectral and multitemporal remote sensing images according to both qualitative and quantitative analyses. The results obtained confirm the interest of the proposed framework and the validity of the related theoretical analysis

This paper presents a split-based approach (SBA) to automatic and unsupervised change detection in large-size multitemporal remote-sensing images. Unlike standard methods that are presented in the literature, the proposed approach can detect in a consistent and reliable way changes in images of large size also when the extension of the changed area is small (and, therefore, the prior probability of the class of changed pixels is very small). The method is based on the following: 1) a split of the large-size image into subimages; 2) an adaptive analysis of each subimage; and 3) an automatic split-based threshold-selection procedure. This general approach is used for defining a system for damage assessment in multitemporal synthetic aperture radar (SAR) images. The proposed system has been developed to properly identify different levels of damages that are induced by tsunamis along coastal areas. Experimental results that are obtained on multitemporal RADARSAT-1 SAR images of the Sumatra Island, Indonesia, confirm the effectiveness of both the proposed SBA and the presented system for tsunami-damage assessment

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. This technique is based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times. Each spatial position in the considered scene is represented by a neuron in the Hopfield network that is connected only to its neighboring units. These connections model the spatial correlation between neighboring pixels and are associated with a context-sensitive energy function that represents the overall status of the network. Change detection maps are obtained by iteratively updating the output status of the neurons until a minimum of the energy function is reached and the network assumes a stable state. A simple heuristic thresholding procedure is presented and adopted for initializing the network. The proposed change detection technique is unsupervised and distribution free. Experimental results carried out on two multispectral and multitemporal remote sensing images confirm the effectiveness of the proposed technique

In this paper a framework for a formal definition and a theoretical study of the change vector analysis (CVA) technique is proposed. This framework, which is based on the representation of the CVA in polar coordinates, aims at: (i) introducing formal definitions in the polar domain for a better general description and understanding of the information present in spectral change vectors; (ii) analyzing from a theoretical point of view the distributions of changed and unchanged pixels in the polar domain; (iii) driving the implementation of proper preprocessing procedures for multitemporal images on the basis of the results of the theoretical study on the distributions; and (iv) defining a solid background for the development of advanced and accurate automatic change-detection algorithms in the polar domain. The findings derived from the theoretical analysis on the statistical models of classes have been validated on real multispectral and multitemporal remote sensing images.

In this paper, a novel context-sensitive classification technique based on Support Vector Machines (CS-SVM) is proposed. This technique aims at exploiting the promising SVM method for classification of 2-D (or n-D) scenes by considering the spatial-context information of the pixel to be analyzed. In greater detail, the proposed architecture properly exploits the spatial-context information for: i) increasing the robustness of the learning procedure of SVMs to the noise present in the training set (mislabeled training samples); ii) regularizing the classification maps. The first property is achieved by introducing a context-sensitive term in the objective function to be minimized for defining the decision hyperplane in the SVM kernel space. The second property is obtained including in the classification procedure of a generic pattern the information of neighboring pixels. Experiments carried out on very high geometrical resolution images confirm the validity of the proposed technique.

A novel adaptive multiscale approach to unsupervised change detection in multitemporal synthetic aperture radar (SAR) images is proposed. This approach is based on a multiresolution decomposition of the log-ratio image (obtained by a comparison of a pair of co-registered images acquired at different times on the same area) in a set of scale-dependent images characterized by a different trade-off between speckle reduction and preservation of geometrical details. For each pixel to be analyzed, a sub-set of reliable scales is identified according to an automatic local analysis of the statistic of the data. The final change-detection map is obtained according to an adaptive scale-driven fusion algorithm, which properly exploits the results of the analysis at different scales for producing an accurate and reliable change-detection map in both homogeneous and border areas. Experimental results confirm the effectiveness of the proposed technique.

This letter presents a novel parcel-based context- sensitive technique for unsupervised change detection in very high geometrical resolution images. In order to improve pixel-based change-detection performance, we propose to exploit the spatial- context information in the framework of a multilevel approach. The proposed technique models the scene (and hence changes) at different resolution levels defining multitemporal and multilevel (i.e., small homogeneous regions shared by both original images). Change detection is achieved by applying a multilevel change vector analysis to each pixel of the considered images. This technique properly analyzes the multilevel and multitem- poral parcel-based context information of the considered spatial position. The adaptive nature of multitemporal parcels and their multilevel representation allow one a proper modeling of complex objects in the investigated scene as well as borders and details of the changed areas. Experimental results confirm the effectiveness of the proposed approach.

This paper presents a novel approach to change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach exploits a wavelet-based multiscale decomposition of the log-ratio image (obtained by a comparison of the original multitemporal data) aimed at achieving different scales (levels) of representation of the change signal. Each scale is characterized by a different tradeoff between speckle reduction and preservation of geometrical details. For each pixel, a subset of reliable scales is identified on the basis of a local statistic measure applied to scale-dependent log-ratio images. The final change-detection result is obtained according to an adaptive scale-driven fusion algorithm. Experimental results obtained on multitemporal SAR images acquired by the ERS-1 satellite confirm the effectiveness of the proposed approach.