Semantic change detection (SCD) aims to identify potential Earth surface changes, including their location and class, from multi-temporal remote sensing images. However, the under-detection and pseudo-change issues in existing SCD methods severally limit their effectiveness in diverse ground scenarios. To address these issues, a semantic map-guided network, namely SMGNet, is proposed based on a multitask architecture designed to identify potential land cover changes from bi-temporal high-resolution remote sensing images. A robust feature extractor is first developed to extract multi-scale contextual information while retaining fine-grained spatial details, thus enhancing the semantic representation of complex objects with irregular shapes and large sizes. To address the issue of under-detection, we integrate historical semantic information derived from pre-temporal land cover maps into the model using a semantic map encoder module. A semantic fusion module based on Bayesian theory is developed to highlight salient changed information, thus reducing pseudo-changes caused by the same ground objects with spectra variations. Experimental results obtained in a public SCD dataset demonstrate the effectiveness of the proposed method in identifying various semantic changes. Results indicate that the proposed SMGNet achieved the highest detection accuracy, exceeding nine existing methods by 14.81% to 41.28% and 8.45% to 40.31% in terms of SeK andF1scdmetrics on the HRSCD dataset, respectively. The proposed method effectively alleviated pseudo-changes induced by spectra and temporal differences, and accurately detecting these changed objects with irregular shapes and large sizes. The detected results exhibited high inter-class compactness and well-defined boundaries. Code and data are available at https://github.com/long123524/SMGNet.

Accurate lunar rock detection is vital for lunar exploration. However, the existing methods are sensitive to factors, such as the uneven lighting and terrain relief. To address these issues, a novel method combining multiscale phase feature type maps (PFTMs) and phase congruency moment maps (PCMMs) is proposed. First, rock seeds are detected through phase congruency and gradient analysis. Second, a strategy called the local scale saliency score (LSSS) is proposed to adaptively estimate the optimal scale layer for candidate rock detection. Within this layer, the specifically designed local-contextual and global-contextual (LGC) features are employed to identify the regions of interests (ROIs) for rocks. Subsequently, the process of filtering false positives (FPs) involves the utilization of geometric metrics and scale feature analysis. Finally, a specially designed edge detector named the bilateral local maximum PCMM-PFTM path is proposed to describe the edges of the rocks. Tests on Chang’E-3 and Chang’E-5 Landing Camera (LCAM) images show the proposed method’s robustness in detecting lunar rocks of varying sizes and reflectance, achieving F1-scores ranging from 0.919 to 0.947.

Accurate change detection (CD) in multitemporal multimodal remote sensing images is crucial for numerous applications. However, existing unsupervised CD methods often face challenges in suppressing background noise, preserving fine-grained boundaries, and maintaining spatial coherence of target regions. To overcome these limitations, this study proposes a novel Scribble-Guided Structural Regression Fusion (SG-SRF) framework, which integrates sparse scribble annotations as lightweight priors into a dynamic regression mechanism. Specifically, the framework employs a scribble distance map to refine hypergraph Laplacian matrices, thereby optimizing feature representation for critical targets while suppressing irrelevant backgrounds. Experimental results demonstrate that the proposed method significantly outperforms traditional unsupervised methods in detecting complete and accurate change objects with minimal scribble input. Notably, the scribble guidance offers an efficient and cost-effective solution to the inherent limitations of unsupervised approaches, enabling more precise change detection without extensive labeled datasets. This work aims to bridge the gap between unsupervised adaptability and supervised accuracy, offering significant potential for practical CD applications. The source code will be made publicly available at https://github.com/MissYongjie/SG-SRF.

The mineral composition of lunar regolith influences brightness temperature (TB) as observed by the microwave radiometer (MRM); however, the large-scale spatial relationship between TB and mineral abundance has yet to be sufficiently revealed. This study aims to quantify the impact of specific mineral abundances (plagioclase and ilmenite) on TB distribution, using MRM 37-GHz data from Chang’E-2 and mineral abundance products from Kaguya. We applied hour angle correction and latitude normalization to produce high-accuracy TB maps and developed a self-adaptive moving-window method to remove strip noise to produce higher precision mineral abundance maps. Using these two types of maps, we performed comprehensive large-scale spatial analysis of TB and mineral abundance using a bivariate spatial autocorrelation model and a geographically weighted regression (GWR) approach considering spatial similarity and heterogeneity, respectively. The bivariate analysis indicates a negative spatial correlation between TB and plagioclase abundance, while a positive spatial correlation between TB and ilmenite abundance. In addition, bivariate anomalies, including both hot and cold spots of diurnal TB amplitude (i.e., noon minus nighttime), were identified through the simultaneous consideration of TB and mineral abundance. The GWR analysis reveals regional variations in the impact of mineral abundances on diurnal TB amplitudes. These correlations can be attributed to the spatial distributions and variations in TB, which arise from the unique dielectric and thermal properties of minerals across distinct regions on the Moon. These findings contribute to a better understanding of subsurface thermal behavior and regimes, enhancing our comprehension of lunar evolution.

On May 15, 2021, China’s first Mars rover, the Zhurong rover successfully landed on the Utopian Planitia in the northern region of Mars. The multispectral camera (MSCam) on board the rover has captured multi-spectral images, which provide spatial and spectral information about in-situ observation targets and facilitate analysis of the types of materials on the Martian surface. However, frequent sandstorms on Mars are accompanied by dust deposition, and varying degrees of dust coverage have altered the original spectral characteristics of scientific detection targets, resulting in inaccurate material inversion. To address this issue, a novel in-situ dust cover index (IDCI) is proposed. The data-driven method is based on the spectral features of dust cover in the MSCam multispectral bands. It provides a wealth of information through a simple yet effective calculation that maximizes the discrimination between different categories of dust-impacted areas and estimates the degree of dust coverage. Experimental results obtained from 17 scientific observations by MSCam along the Zhurong rover’s routing path confirm the effectiveness of the proposed IDCI. It effectively distinguished dust-free areas from invalid areas (e.g., shadows), while reflecting the degree of dust coverage in the scene. The IDCI demonstrated its superior performance, operating up to three times faster than other reference methods. Additionally, it exhibited a notable advantage over other techniques, achieving a variance ration criterion (VRC) for target separation that was at least 5% higher. These results highlight the efficiency and effectiveness of the proposed IDCI, establishing it as a valuable tool for Martian surface analysis.

The Martian surface landforms are highly related to the safe landing and traversability of Mars rovers. Furthermore, landforms associated with the presence of water/ice, minerals and biosignatures can provide valuable insights for Mars exploration missions, particularly in relation to the selection of landing or sample collection sites. The small number of Martian landform datasets and the scarcity of labelable landform samples over Mars make the precise mapping of Martian landforms a challenging task. In this article, we propose a stepwise deep feature transfer (SDFT) model for the mapping of Martian landforms with a small number of labeled samples. The SDFT model comprises two transfer steps. In the first transfer step, a deep learning model trained on a large public source dataset from Earth is transferred to a medium sized public dataset from Mars. This transfer is conducted through a standard pre-training and fine-tuning procedure utilizing a linear classifier. In the second transfer step, the model is further transferred to a small number of target datasets on Mars through a pre-training and fine-tuning procedure with a cosine distance classifier. The stepwise training technique mitigates the challenges associated with varying datasets and small training samples. The proposed SDFT model has been validated on two self-built sample sets using images from the Mars Reconnaissance Orbiter’s Context Camera (CTX). It has also been employed for landform mapping in two local regions with small samples to evaluate its effectiveness in comparison with existing state-of-the-art methods.

Multimodal remote sensing change detection (MRS-CD) is essential for many geospatial applications. However, it remains challenging due to spectral heterogeneity, temporal misalignment, and inconsistencies between different data sources. Existing MRS-CD methods, particularly those employing graph structures, often suffer from parameter sensitivity, limited generalization across datasets, and difficulty in detecting fine-grained changes. Meanwhile, current weakly supervised approaches typically rely on image-level annotations, which often result in coarse localization, blurred boundaries, and limited transferability across heterogeneous modalities of changes. In this work, we present SHIFT (Scribble-driven Hybrid Iterative Feature Tracker), a weakly supervised MRS-CD approach guided by sparse scribble annotations. SHIFT employs a scribble-driven feature extraction module to extract multimodal features, converting scribble annotations into smooth attention maps with a learnable Gaussian blur module to focus the model on potential change areas. Through iterative refinement, SHIFT enhances detection confidence in change regions. Experiments on 13 public datasets demonstrate the superior performance of the proposed approach compared to a number of state-of-the-art methods. Our approach achieves high accuracy with minimal annotations and robust generalization across diverse multimodal scenarios. The source code will be made publicly available at https://github.com/MissYongjie/SHIFT.

The development of satellite and airborne sensor technologies has resulted in a wealth of multisource remote sensing images, which have great potential for precise analysis and monitoring of the Earth’s surface. Despite recent advancements in change detection (CD) on multitemporal remote sensing images, there are still many challenges in effectively integrating multimodal remote sensing data in the CD task. To address these challenges, this paper proposes a novel unsupervised Transformer-enhanced Encoder-Decoder CD (namely ETD-CD) framework for heterogeneous remote sensing images. The proposed framework focuses on fusion of multimodal features for achieving high-precision CD results without relying on high-quality manual labels. Experimental results demonstrate the superiority of the proposed ETD-CD network for multimodal remote sensing image CD.

The positioning accuracy of synthetic aperture radar (SAR) images is affected by factors, such as satellite platform instability, aging of on-board instruments, and environmental changes. Geometric calibration is a commonly employed and cost-effective method to enhance the positioning accuracy of SAR images. The classical point-based geometric calibration (PB-GC) model, however, only utilizes the location of ground control points (GCPs) and does not fully exploit the spatial relationships among the GCPs. This study introduces a high-precision geometric calibration method that builds upon the classical model for calibrating SAR imaging systems. This method incorporates the Co-Line-GC and Co-Circle-GC models, where the former uses GCPs distributed on a line while the latter uses GCPs distributed on a circle. The results reveal that, compared to the classical model, our approach enhances the positioning accuracy of Gaofen-3 and Sentinel-1A SAR images by approximately 2 m in eastern China, achieving a mean positioning accuracy of 3.02 m. In terms of calibration performance, a comparison between postcalibrated and precalibrated images indicates that the images are shifted, not distorted, and a better match of the same features between different scenes in the image mosaic is observed after calibration. The improved positioning accuracy of SAR images significantly contributes to global remote sensing mapping, land use change monitoring, and ground target detection applications.

The investigation of small lunar craters holds scientific and engineering significance. This paper presents a novel method for detecting small lunar craters. It consists of three stages: seed detection, candidate crater detection, and crater evaluation. Firstly, crater seeds are identified through morphological operations as pixels with the highest local gradient and specific gradient direction. Secondly, the optimal scale for each seed is estimated based on the maximum response of the established phase congruency maximum moment (PCMM) scale space. For detecting very small craters, a crater detector called statistical morphological constraint path-sets (SMPS), which leverages image spatial domain features, is proposed. It configures the image as a weighted directed graph, using path-sets centered on seeds to flexibly detect highlights and shadow regions of craters. For detecting craters with larger optimal scale, another crater detector named structural consistency constrained multi-paths (SCMP) is proposed, utilizing the frequency phase features. The core idea of SCMP is to configure the phase feature type (PFT) map with the optimal scale as a directed graph. Centered on the seed, the multi-path operator is designed to detect craters. Unsupervised discriminative correlation filters (UDCFs) are trained with HOG features from images or PFT maps to validate candidate craters. The results indicate that for images with a resolution of 0.5-2m/pixel, the proposed method demonstrates good detection performance for small craters with diameters of less than 5 m, 5-10 m, and greater than 10 m, with an average detection rate of 0.89, 0.91, and 0.92, respectively.

In situ detection on Mars can provide detailed information on the planet’s topography and material composition while also validating the results obtained by orbiter probes. The laser-induced breakdown spectroscopy (LIBS) has emerged as a popular technology for Mars in situ exploration due to its fast response and high accuracy in identifying elements. The analysis of LIBS data obtained by different in situ scientific payloads onboard Mars rovers can help explain scientific problems related to about Martian geological genesis and history. However, it is essential to correct the data acquired by different instruments for joint analysis and to facilitate scientific discoveries due to variances in instrument specifications and data acquisition conditions. This article presents a novel cross-instrument spectral harmonization (CISH) approach that can eliminate differences in intensity and peak positions in LIBS spectra from different instruments. In particular, a peak position consistency correction (P2C2) method is proposed to correct cross-instrument peak position inconsistency by eliminating noise or irregular bumps presented in the LIBS spectra that may be incorrectly identified as characteristic peaks. The proposed CISH approach was validated using real Mars in situ LIBS data acquired by the chemistry and camera tool (ChemCam) and Mars surface composition detector (MarSCoDe). The experimental results demonstrate increased consistency in intensity and peak positions. Specifically, the average intensity difference decreased from 3.1287 to 2.1898, and the average peak position difference decreased from 0.1540 to 0.0335 nm. Meanwhile, the accuracy of inversion after consistency correction for the same calibration target (Norite) was also improved. The average root-mean-square error (RMSE) of eight oxides decreased from 5.18 to 3.37 by using a support vector machine (SVM) and from 18.80 to 7.07 using a partial least squares-submodel (PLS-SM). The proposed approach has the potential to establish a uniform benchmark for LIBS data acquired by different instruments at different times and locations, ensuring data consistency and comparability of identified material composition results.

Landform classification and mapping of the Martian surface using Mars orbiter images can provide an important reference for landing site selection and rovers’ traversability evaluation in Mars exploration. Moreover, specific Martian landforms are closely associated with the evidences of water-related activities and Martian life, thus have crucial research importance. This article proposes a novel superpixel-guided multiview feature fusion network (MarsMapNet) for efficient mapping of the Martian landforms. In particular, the proposed MarsMapNet first generates the superpixel-level segments from Mars orbiter images by considering local morphological homogeneity of landforms. Then, a multiview feature extraction and fusion (MVF) network is developed, where abstract convolutional features are extracted based on scene-level patches, and multitextures are extracted based on local landform from shallow-to-deep feature learning. After the network being trained on scene-level samples and guided by the superpixel segmentation, Martian landforms can be correctly classified in an efficient way, whose mapping time cost sharply decreased when compared to the reference methods. The proposed MarsMapNet has been validated on three real landing sites from several Mars missions (i.e., the Jezero Crater, the Southern Utopia Planitia, and the Oxia Planum) by using the Mars Reconnaissance Orbiter’s Context Camera (CTX) images. Qualitative and quantitative analyses on the obtained experimental results confirm the effectiveness and efficiency of the proposed MarsMapNet when compared with the state-of-the-art (SOTA) methods, demonstrating its potential for supporting a Martian global landform mapping in the future.

With the increasing number of high resolution (HR) images captured by various platforms, integrating spectral and spatial properties of data across different HR image types, such as multispectral (MS), hyperspectral (HS), and multitemporal (MT) images, remains a challenging task for object classification. This paper proposes a novel hybrid framework named Hybrid FusionNet (HFN) that jointly exploits 2D-3D Convolutional Neural Networks (CNNs) and a Transformer encoder to address a complex classification problem. By incorporating 2D and 3D convolutional layers, the proposed HFN generates rich multi-dimensional hybrid features, including spectral, spatial, and temporal features. These features are then fed into a Transformer encoder to learn global saliency and discriminative information, enabling the identification of spatially irregular and spectrally similar objects. The hybrid architecture efficiently captures local intricate spectral-spatial-temporal contextual features through convolutional layers. Then it learns global long-range dependencies and the spectral dimension through the Transformer encoder, thus effectively reducing spectral-spatial mutations, distortions, and variations of ground objects. Experimental results from an HR-MS dataset, an HR-HS dataset, and an HR-MT dataset covering complex urban scenarios confirm the effectiveness of the proposed approach compared to the main state-of-the-art methods. Notably, the proposed HFN can achieve satisfactory classification performance even with limited training samples. The source code will be made available at https://github.com/MissYongjie/Hybrid-FusionNet.

In the field of change detection, detecting changes in images with different resolutions is crucial for both long-term interval scenes and scenarios that require rapid detection. However, existing methods face two main issues. Firstly, they require more stringent prior knowledge. The SPM-based methods require pixel-level class labeling of high-resolution(HR) images, while the approaches based on image super-resolution require HR images corresponding to low-resolutionr(LR) images. Such prior knowledge is either costly for labeling or difficult to meet in realistic scenarios. The second is that redundant error accumulation affects detection accuracy. Whether in traditional sub-pixel mapping(SPM) methods or deep learning methods based on image super-resolution, the final detection results are obtained after generating HR images from LR images. The redundant error produced in this step will be accumulated and affect the final detection results. Although the unsupervised methods do not have this problem, the detection accuracy is not as good as that of the supervised. To address these issues, we propose a multi-scale Transformer network(MTN). This model first uses a multi-scale feature extractor(MFE) to extract multi-scale features and perform scale matching at the feature level. Then, the Transformer is used to extract long-range relationships of ground objects on the multi-scale features to enhance the features. Finally, the multi-scale features are fused, and a classifier composed of a convolutional network is used to obtain binary change detection results. In addition, we consider that the edges of objects may be affected during the scale matching process, and introduce a CEBoundary Loss to better detect object edges. The results on the LEVIR and Google datasets demonstrate the effectiveness of our proposed method. The source code of MTN is available at https://github.com/Gavin-debug/MultiResolutionCD.

In recent years, numerous deep learning (DL)-based frameworks have been proposed for hyperspectral image classification (HSIC). Considering a large number of spectral bands of hyperspectral images (HSIs), it is still challenging to effectively utilize the spectral information and achieve accurate classification when few training samples are available. To make full use of the spectral-spatial information in HSIs with few training samples, in this letter we propose a lightweight end-to-end attention-enhanced feature fusion network (AeF2N). The proposed AeF2N consists of four sequential stages, i.e., spectral feature augmentation, spatial contextual feature interaction, spectral feature augmentation, and classification. The first and third stages are used to capture and augment the discriminative spectral features, while the second stage is used to capture spatial information. Notably, two novel attention blocks, spectral augmentation attention (SAA) and spatial integration attention (SIA) are interactively introduced to capture significant spectral and spatial information, respectively. Based on the proposed spectral and spatial feature discrimination stages, the AeF2N effectively identifies both spectrally significant (e.g., irregular small objects) and spatially significant (e.g., specific-shaped objects) land objects with high accuracy. Experimental results obtained on three benchmark hyperspectral datasets demonstrate the superiority of the proposed approach compared with six state-of-the-art DL-based methods in terms of higher classification accuracy and efficiency.

Change detection is a process of identifying changed ground objects by comparing image pairs obtained at different times. Compared with the pixel-level and object-level change detection, scene-level change detection can provide the semantic changes at image level, so it is important for many applications related to change descriptions and explanations such as urban functional area change monitoring. Automatic scene-level change detection approaches do not require ground truth used for training, making them more appealing in practical applications than nonautomatic methods. However, the existing automatic scene-level change detection methods only utilize low-level and mid-level features to extract changes between bitemporal images, failing to fully exploit the deep information. This article proposed a novel automatic binary scene-level change detection approach based on deep learning to address these issues. First, the pretrained VGG-16 and change vector analysis are adopted for scene-level direct predetection to produce a scene-level pseudo-change map. Second, pixel-level classification is implemented by using decision tree, and a pixel-level to scene-level conversion strategy is designed to generate the other scene-level pseudo-change map. Third, the scene-level training samples are obtained by fusing the two pseudo-change maps. Finally, the binary scene-level change map is produced by training a novel scene change detection triplet network (SCDTN). The proposed SCDTN integrates a late-fusion subnetwork and an early fusion subnetwork, comprehensively mining the deep information in each raw image as well as the temporal correlation between two raw images. Experiments were performed on a public dataset and a new challenging dataset, and the results demonstrated the effectiveness and superiority of the proposed approach

Scene change detection identifies functional changes at the scene level. Compared with pixel-level and object-level change detection, it can provide a higher level understanding of changes on the Earth’s surface. Triple-branch networks that perform scene binary change detection and scene classification tasks simultaneously are competitive in the field of scene change detection, as they consider both single-temporal scene semantic information and cross-temporal change features. However, some problems still exist. First, the temporal change feature extraction is insufficient, and the 1-D feature vector used for scene change detection and classification is lacking in representativeness. Second, the predicted scene binary change detection and classification results are often contradictory at the network prediction stage, leading to the unsatisfactory performance of change trajectory identification. To address these issues, a novel framework that integrates a differential aggregation network (DAN) and class probability-based fusion strategy (CPFS) was proposed. The designed DAN can fully capture the temporal change features using four advanced differential fusion modules (DFMs) to aggregate the multilevel difference information. In addition, it is able to generate more representative 1-D feature vectors by adopting two novel attention-aware adaptive pooling modules (AAPMs). The developed CPFS produces the final consistent scene binary change detection and classification maps by fusing three predicted class probability vectors. The proposed method was validated on two datasets, and the results demonstrated its superiority to the comparison methods.

Deep metric learning methods have recently drawn significant attention in the field of remote sensing (RS), owing to their prominent capabilities for modeling relations among RS images based on their semantic contents. In the context of scene classification and large-scale image retrieval, one of the most prominent deep metric learning methods is the scalable neighborhood component analysis (SNCA), which has demonstrated excellent performance on the locality neighborhood structure in the metric space. However, the standard SNCA has important constraints on separating the hard positive and other negative images in the metric space, and this may become a major limitation when dealing with the large-scale variance problem inherent to RS data. To address this issue, we propose a novel deep metric learning formulation that introduces a new margin parameter to enforce the compactness of the within-class feature embeddings. Based on this innovative scheme, we propose two novel loss functions: 1) T-SNCA-c, where the parameter is based on the cosine similarity, and 2) T-SNCA-a, where the parameter is based on the angular distance. Besides, we exploit memory bank optimization to further enhance the semantic diversity during training. Our experimental results, conducted using three downstream applications ($K$-NN classification, clustering, and image retrieval) and two large-scale RS benchmark datasets, demonstrate that the proposed approach can achieve superior performance when compared to current state-of-the-art deep metric learning methods. The codes of this work will be made available online (https://github.com/jiankang1991/GRSL_TSNCA).

Accurate detection of the multiple classes in aerial images has become possible with the use of anchor-based object detectors. However, anchor-based object detectors place a large number of preset anchors on images and regress the target bounding box while anchor-free object detections predict the location of objects directly and avoid the carefully predefined anchor box parameters. Object detection in aerial images is faced with two main challenges: 1) the scale diversity of the geospatial objects; and 2) the cluttered background in complex scenes. In this letter, to address these challenges, we present a novel Anchor-Free Network with a Density map and attention mechanism (DA2FNet). Considering the extreme density variations of the detection instances among the different categories in aerial images, the proposed DA2FNet model conducts density map estimation with image-level supervision for the geospatial object counting, to acquire global knowledge about the scale information. A simple and effective image-level global counting loss function is also introduced. In addition, a compositional attention network is further introduced to enhance the saliency of the foreground objects. The proposed DA2FNet method was compared with the state-of-the-art object detection models, achieving excellent performance on the NWPU VHR-10, RSOD, and DOTA datasets.

In this letter, CatBoost was first introduced and investigated for remote sensing (RS) image classification using diverse features. To improve the classification performance by fostering the effective and efficient spatial feature extraction, a new pseudo label features (PLFs) extraction method was proposed via multisize neighboring patches-based multiclustering. Experimental results on two hyperspectral and one PolSAR benchmarks showed that: 1) CatBoost is an advanced ensemble learning (EL) algorithm for classification of RS images using diverse features; 2) CatBoost has better capability of reducing the overfitting issue at large number of boosting iteration; and 3) proposed PLFs can result in compatible and even better classification results than using morphological profiles (MPs) and MPs with partial reconstruction (MPPR) spatial features.

Continuous change detection (CCD) is one of the most famous algorithms in remote sensing time series change detection, making any improvements on it are of great significance for the practice of monitoring land cover dynamics. Inspired by the directionality of CCD, a bidirectional CCD (Bi-CCD) is proposed to improve the accuracy of change detection by selecting the optimal result from the results of CCD running from both the forward and backward directions of time series. Three criteria including root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were adopted to define the “optimal” in this study, and their effects under three common parameter settings were evaluated in a simulation dataset. The quantitative results show that Bi-CCD based on different result selection criteria can indeed improve the accuracy of change detection in terms of omission error and commission error. In general, Bi-CCD based on RMSE achieved the lowest omission error, while Bi-CCD based on BIC obtained the lowest commission error. Compared with the unidirectional CCD, Bi-CCD reduces the omission error and commission error by at most 11.19% and 15.08%, respectively. In addition, the different effects of different optimal result selection criteria on the accuracy of Bi-CCD enable Bi-CCD more flexible to handle different tasks than the standard CCD.

Semantic change detection (SCD) extends the multiclass change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many applications. Recent studies indicate that the SCD can be modeled through a triple-branch convolutional neural network (CNN), which contains two temporal branches and a change branch. However, in this architecture, the communications between the temporal branches and the change branch are insufficient. To overcome the limitations in existing methods, we propose a novel CNN architecture for the SCD, where the semantic temporal features are merged in a deep CD unit. Furthermore, we elaborate on this architecture to reason the bi-temporal semantic correlations. The resulting bi-temporal semantic reasoning network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations, as well as a novel loss function to improve the semantic consistency of change detection results. Experimental results on a benchmark dataset show that the proposed architecture obtains significant accuracy improvements over the existing approaches, while the added designs in the Bi-SRNet further improve the segmentation of both semantic categories and the changed areas. The codes in this article are accessible athttps://github.com/ggsDing/Bi-SRNet.

With the increasing availability and resolution of satellite sensor data, multispectral (MS) and panchromatic (PAN) images are the most popular data that are used in remote sensing among applications. This article proposes a novel cross-resolution hidden layer feature fusion (CRHFF) approach for joint classification of multiresolution MS and PAN images. In particular, shallow spectral and spatial features at a global scale are first extracted from an MS image. Then, deep cross-resolution hidden layer features extracted from MS and PAN are fused from patches at a local scale according to an autoencoder (AE)-like deep network. Finally, the selected multiresolution hidden layer features are classified in a supervised manner. By taking advantage of integrated shallow-to-deep and global-to-local features from the high-resolution MS and PAN images, the cross-resolution latent information can be extracted and fused in order to better model imaged objects from the multimodal representation and finally increase the classification accuracy. Experimental results obtained on three real multiresolution datasets covering complex urban scenarios confirm the effectiveness of the proposed approach in terms of higher accuracy and robustness with respect to literature methods.

With more detailed spatial information being represented in very-high-resolution (VHR) remote sensing images, stringent requirements are imposed on accurate image classification. Due to the diverse land-objects with intraclass variation and interclass similarity, efficient and fine classification of VHR images especially in complex scenes is challenging. Even for some popular deep learning (DL) frameworks, geometric details of land-object may be lost in deep feature levels, so it is difficult to maintain the highly-detailed spatial information (e.g., edges, small objects) only relying on the last high-level layer. Moreover, many of the newly developed DL methods require massive well-labeled samples, which inevitably deteriorates the model generalization ability under the few-shot learning. Therefore, in this paper, a lightweight shallow-to-deep feature fusion network (SDF2N) is proposed for VHR image classification, where the traditional machine learning (ML) and DL schemes are integrated to learn rich and representative information to improve the classification accuracy. In particular, the shallow spectral-spatial features are first extracted, and then a novel triple-stage fusion (TSF) module is designed to learn the saliency and discriminative information at different levels for classification. The TSF module includes three feature fusion stages, i.e., low-level spectral-spatial feature fusion, middle-level multi-scale feature fusion, and high-level multi-layer feature fusion. The proposed SDF2N takes advantages of the shallow-to-deep features, which can extract representative and complementary information of crossing layers. It is important to note that even with limited training samples, the SDF2N still can achieve satisfying classification performance. Experimental results obtained on three real VHR remote sensing data sets including two multispectral and one airborne hyperspectral images covering complex urban scenarios confirm the effectiveness of the proposed approach compared with the state-of-the-art methods.

Crater detection from planetary images is a challenging issue due to the complicated variations in geometry shape, illumination, and scale. An automatic crater detection algorithm (CDA) that is robust to these factors is, therefore, necessary. In this article, a novel automatic CDA that is robust to these factors is proposed to detect the multiscale craters of the Moon. The proposed method consists of two main steps: 1) in the hypothesis generation (HG) step, a novel feature operator called the path-profile, which is constructed based on the self-defined adjacency graph and a path descriptor, is presented to derive the highlight-shadow feature of craters for detecting candidate craters. 2) In the hypothesis verification (HV) step, based on the idea of anomaly detection, the isolation forest algorithm which is an unsupervised learning anomaly detection method is applied to eliminate falsely detected craters. Lunar Reconnaissance Orbiter Camera Wide Angle Camera and Narrow Angle Camera images and Chang’E-4 landing camera images were used to test the accuracy and robustness of the proposed method. The experimental results indicate that: on average, the accuracy of the detection result of the HG step is about 90%, and the HV step can further improve this by 3%–4%. The proposed method is a reliable way to detect multiscale lunar craters for various resolutions images with diameters ranging from five pixels to hundreds of pixels, and it is robust to the different terrains and illumination conditions on the Moon.

Among various multimodal remote sensing data, the pairing of multispectral (MS) and panchromatic (PAN) images is widely used in remote sensing applications. This article proposes a novel global collaborative fusion network (GCFnet) for joint classification of MS and PAN images. In particular, a global patch-free classification scheme based on an encoder-decoder deep learning (DL) network is developed to exploit context dependencies in the image. The proposed GCFnet is designed based on a novel collaborative fusion architecture, which mainly contains three parts: 1) two shallow-to-deep feature fusion branches related to individual MS and PAN images; 2) a multiscale cross-modal feature fusion branch of the two images, where an adaptive loss weighted fusion strategy is designed to calculate the total loss of two individual and the cross-modal branches; 3) a probability weighted decision fusion strategy for the fusion of the classification results of three branches to further improve the classification performance. Experimental results obtained on three real datasets covering complex urban scenarios confirm the effectiveness of the proposed GCFnet in terms of higher accuracy and robustness compared to existing methods. By utilizing both sampled and non-sampled position data in the feature extraction process, the proposed GCFnet can achieve excellent performance even in a small sample-size case. The codes will be available from the website: https://github.com/SicongLiuRS/GCFnet.

Convolutional neural networks (CNNs) have achieved great success when characterizing remote sensing (RS) images. However, the lack of sufficient annotated data (together with the high complexity of the RS image domain) often makes supervised and transfer learning schemes limited from an operational perspective. Despite the fact that unsupervised methods can potentially relieve these limitations, they are frequently unable to effectively exploit relevant prior knowledge about the RS domain, which may eventually constrain their final performance. In order to address these challenges, this article presents a new unsupervised deep metric learning model, called spatially augmented momentum contrast (SauMoCo), which has been specially designed to characterize unlabeled RS scenes. Based on the first law of geography, the proposed approach defines spatial augmentation criteria to uncover semantic relationships among land cover tiles. Then, a queue of deep embeddings is constructed to enhance the semantic variety of RS tiles within the considered contrastive learning process, where an auxiliary CNN model serves as an updating mechanism. Our experimental comparison, including different state-of-the-art techniques and benchmark RS image archives, reveals that the proposed approach obtains remarkable performance gains when characterizing unlabeled scenes since it is able to substantially enhance the discrimination ability among complex land cover categories. The source codes of this article will be made available to the RS community for reproducible research.

In active learning (AL)-based remote sensing (RS) image classification tasks, the acquisition of labeled data depends not only on the informativeness and representativeness measured in feature space but also on the spatial distributions and relations in an image plane. However, very few studies have investigated the advantages of integrating spatial constraints into the AL paradigm. Hence, under the basic assumption “instances that are difficult to classify are usually located around edges between different objects or land-cover types,” edge gradient information was integrated into the conventional AL paradigm using popular uncertainty and diversity measurements. The experimental results with two real hyperspectral images confirmed the advantages of the proposed edge gradient-based AL (EGAL) approach from the aspects of fast convergence and computationally efficient operation.

Fine co-registration that precisely aligns multiple images acquired over a given area is an important process to exploit the very high resolution (VHR) multitemporal images in a wide range of remote sensing applications. The objective of this study is to analyze the effect of the fine co-registration performance on an unsupervised change detection between VHR images. To this end, we extract registration noise (RN) samples, which are denoted as misaligned pixels in a local region. Then, the location of conjugate points (CPs) is positioned by analyzing the local distribution of the extracted RN samples. The CPs are employed for generating a non-rigid transformation model to warp a sensed image into a reference image. An unsupervised change vector analysis approach is used to validate the effectiveness of the proposed fine co-registration performance. Experiments are implemented on a Worldview-3 VHR multispectral dataset.

The spatio-temporal pattern of cultivated land and its changes are of great significance in the study of ecology, geography and agronomy. In this study, long time series remote sensing information is used to monitor the spatio-temporal pattern and phenological characteristics of cultivated land on Chongming Eco-island, Shanghai, China. Based on MODIS-NDVI products (2012-2018), NDVI expectations curves of Chongming Eco-Island are established. It is demonstrated that the negative peaks of NDVI expectation curve in June become less noticeable after 2016. After that, NDVI image difference is performed between the time points at the positive peaks and negative peaks. The results indicate that the reason for the NDVI pattern changes these years may be related to the policy that wheat reduction and green manure enhancement implemented in Chongming Eco-island. Therefore, crops are diversified, and the phenological period of crops on the island is no longer sheer two cropping per year. This study is helpful to the ecological structure adjustment of Chongming Eco-island and the scientific management of cultivated land.

This paper presents a novel multiscale superpixel-guided filter (MSGF) approach for very high resolution (VHR) remote sensing image classification. Different from the traditional guided filter (GF) classification method, the proposed method utilizes a guidance image that constructed from the superpixel segmentation image, which is capable to provide more abundant and accurate edge information of land objects presented in the image. Multiscale features are extracted by the superpixel-guided filter in order to properly model the spatial information of these objects at different scales thus to improve the classification accuracy. Experimental results obtained on a real QuickBird VHR image of Zurich urban scene confirmed the effectiveness of the proposed method.

In this paper, we present an automatic approach for change detection in a large and complex image scenario. The proposed technique takes advantages of automatic registration algorithm and change detection method that jointly measures the spatial invariant but spectral variant features in the considered bitemporal remote sensing image pair. Two classes of pseudo training samples, which associated to the change and no-change two classes, are automatically generated by analyzing the change representation information from both global and local perspectives. Finally, the robust classifier, i.e., linear support vector machine (LSVM), is used to identify the binary changes in the whole image scenario using the pseudo training samples. Experimental results obtained on a pair of real bitemporal Landsat-8 OLI images covering a large scene confirmed the effectiveness of the proposed method.

This paper proposes a novel feature-level fusion approach for burned area change detection at a fine level. The proposed approach relies on two features. The first feature is a modified normalized burn ratio (MNBR) fire index based on Landsat-8 OLI SWIR data, and the second feature is the Bright temperature (BT) based on Landsat-8 TIR data. Then two features are combined by using the gradient transfer fusion algorithm and a change detection technique to generate a fine burned area change map. A real Landsat-8 data set covering a complex fire disaster scenario is utilized to test the performance of the proposed approach. Experimental results demonstrate the effectiveness of the proposed feature-level fusion approach comparing with the reference methods in term of higher separability value and detection accuracy.

The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness.

In this paper a study on the use of multi-temporal LiDAR data to monitor forest changes is presented. LiDAR data acquired at two different times (2007 and 2011) were chosen in order to analyze in details the changes associated to different forest conditions in the temporal domain such as tree growth, tree cuts, and changes in forest volume. The preliminary experimental results show that LiDAR data allow an effective and accurate monitoring of the forest changes.

This paper focuses on solving the multi-class change detection problem in bitemporal multispectral remote sensing images. In that case, information that represented in a small number (e.g., two) of the original bands may be insufficient for the accurate identification of a few of multi-class changes. In particular, this problem becomes more difficult in unsupervised change detection cases when ground reference data is not available. In this paper, a solution is proposed by using the potential information represented in expanded features that constructed from the original spectral bands. Experimental results obtained on a real bitemporal remote sensing data set confirm the effectiveness of the proposed approach.

With the spread of Defense Meteorological Satellite Program (DMSP) data, it has played an important role in fields including urbanization and extraction of urban factors. However, the DMSP nighttime light (NTL) data is saturated in urban centers with high light intensity, resulting in not only the decrease on the DN data in nighttime light data of urban centers, but also recovering the actual light intensity differences within saturated zone. To address the above mentioned problems, we proposed a new index, named the Maximum Vegetation Adjusted NTL Urban Index (MVANUI), which combines vegetation value of Maximum Green Vegetation Fraction product (MGVF) with DMSP NTL value and reduces the effects of the NTL saturation in urban areas. Compared with the original DMSP NTL and VANUI from two aspects, that is, the capacity to distinguish and identify land features inside the saturated zone and the fitting degree of statistical factors in the social economy. Assessments on MVANUI showed that it significantly reduces NTL saturation, has comparatively higher distinguishing ability to special spatial features inside the potential saturated zone and higher relationship with population and GDP, compared with NTL and VANUI.

Image sequences which obtained by close-range videogrammetry, have been widely used in the field of target identification and object tracking. The target identification is one of the key technologies and the corresponding requirement of identification precision are always at a high level in close-range videogrammetry. Therefore, this paper propose a high-precision target identification method for image sequences based on elliptical mark. The proposed approach adopts a coarse-to-fine strategy to identify elliptical mark based on pixel-level detection of Canny method and sub-pixel level identification of Zernike moments, which combines a series of constraint condition methods, thinning method and the least square fitting (LSF). In the process of coarse strategy, the constraint conditions of recursive segmentation method, morphological methods and shape feature are used to remove non-edge points caused by Canny identification. For the fine strategy, the reliable and accurate sub-pixel level edge points are identified by the thinning method and Zernike moments, and sub-pixel center position of elliptical mark are obtained by LSF method. Both simulation experiment and real experiment demonstrated that the proposed method can obtain high precision and reliability results of edge information and center position at a sub-pixel level, and its results are closer to the value by comparing the other two methods in simulation experiment.

The possibility theory, which is an extension of fuzzy sets and fuzzy logic, has shown considerable potential for solving active learning (AL) problems, particularly for multiclass scenarios’ classification. Hence, two recently proposed fuzzy multiclass AL algorithms (classification ambiguity (CA) and fuzzy C‐order ambiguity (FCOA)) are investigated to properly generalise them for classifying hyperspectral images, and two improved versions of the CA and FCOA are proposed. In addition to comparing the performances of the original and improved algorithms, several other state‐of‐the‐art AL methods are evaluated, such as breaking ties, margin sampling, and multi‐class level uncertainty, with or without diversity criteria such as angle‐based diversity (ABD), clustering‐based diversity (CBD), and enhanced clustering‐based diversity (ECBD). Tests on two benchmark hyperspectral images confirm that the proposed improved algorithms are superior to and more effective than the original ones.

Hyperspectral remote sensing image (HSI) clustering can be defined as the process of segmenting pixels into different sets that satisfy the requirement that the differences between sets are much greater than the differences within sets. According to the fast density peak-based clustering algorithm, we propose an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels. For the metric of the density, we present an adaptive-bandwidth probability density function using pixel numbers as the input and the calculated pixel local density as the output, which determines the bandwidth on the basis of the Gaussian assumption. For the metric of the distance, in order to obtain a pixel-level spectral distance, we calculate the Euclidean distance between pixel vectors from the multiple bands. In the proposed approach: 1) use the least-squares method for the curve fitting of the two results; 2) eliminate outliers based on the Pauta criterion; 3) adopt regression calculation; and 4) obtain the cluster centers according to the classification criteria of the local density and the distance between pixel vectors. The other noncluster center points are clustered based on their similarities with the cluster centers by iteration. Finally, we compare the results with those of other unsupervised clustering methods and the reference data sets.

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.

This paper presents a novel semisupervised framework for detecting multi-class changes in bitemporal hyperspectral images. By taking advantages of the state-of-the-art unsupervised change representation technique and the advanced supervised classifiers, the proposed framework allows the generation of pseudo training samples associated with the no-change and each change class that learned from the multitemporal data and import them into the supervised classifiers. Thus multiple changes can be discriminated from the original or the transformed feature space. The proposed approach was validated on a pair of real bitemporal Hyperion hyperspectral images, and the obtained experimental results confirm its effectiveness in addressing the challenging multi-class change detection task in hyperspectral images.

Monitoring urban spatial information is vital to reveal the relationship between the human activity and environment, especially in the Chinese Silk Road Economic Belt, so as to allocate resources reasonably and realize sustainable development. To promote the remote sensing application in this field, a new method was proposed for urban built-up areas extraction mainly based on the support vector machine (SVM) classification with iterative sample refinement, combining Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data, and other auxiliary data such as Landsat images and the GlobeLand30 land cover product. Experiments were conducted by using the proposed approach for several cities in the southwest of the Chinese Silk Road Economic Belt, as classified by statistics and Landsat images. Compared with the traditional threshold dichotomy method and the state-of-the-art improved neighborhood focal statistics (NFS) method, the proposed method achieved better performance with respect to less relative error, and higher overall accuracy and Kappa coefficient.

Oil spill monitoring in optical remote sensing (RS) images is a challenging task due to the complexity of target discrimination in an oil spill scenario. Differently from traditional oil spill detection methods that are mainly carried out in a monotemporal image, in this letter, a novel solution is given in a multitemporal domain by investigating potential capability of change detection (CD) techniques, and it mainly contributes to an unsupervised, semiautomatic, and efficient approach. It opens a new perspective for solving an oil spill detection problem. In particular, a coarse-to-fine multitemporal change analysis procedure is designed to investigate the spectral–temporal variation of change targets present in the scenario. Changes relevant and irrelevant to suspected oil spills are identified and discriminated according to a binary and a multiple CD process, respectively. The proposed approach provides a quick yet effective oil spill detection solution, which is valuable and important in practical applications. The proposed method was validated on two real multitemporal RS data sets presenting the oil spill event in northern Gulf of Mexico in 2010. Experimental results confirmed its effectiveness.

This paper develops a novel oil spill detection approach by using the multitemporal optical remote sensing images. Differently from the traditional oil spill detection methods that mainly carried out on a monotemporal image, the proposed approach opens a new perspective to solve the considered oil spill detection problem in a multitemporal domain by investigating the potential capability of change detection (CD) techniques. A coarse to fine multitemporal change analysis is defined to analyze the spectral-temporal variation of change targets that present in the oil spill scenario. Suspected oil spills and non-relevant changes are identified and discriminated according to a multiple-change detection in the proposed technique. The proposed approach provides a quick, yet effective oil spill detection solution in an unsupervised way, which is valuable and important in practical oil spill detection applications. Experimental results obtained on real HJ-1 satellite images presenting the oil spill event in northern Gulf of Mexico in 2010 confirmed the effectiveness of the proposed method.

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.

Active learning (AL) methods that select unlabeled samples only querying by informative measures (i.e., uncertainty and/or diversity criteria) have been extensively investigated. However, these methods usually do not exploit the manifold structure of the unlabeled data from the geometrical point of view, a choice that might lead to a sample bias and consequently undesirable performances. To control and possibly overcome such drawbacks, this paper explores AL methods based on joint informative and manifold structure representative sampling (JI-MSRS). In JI-MSRS, a portion of the unlabeled samples that are added at each iteration is selected according to the informative measures, whereas another portion is selected according to their capability to represent the data cluster structure. Four popular manifold learning methods, namely, principle component analysis (PCA), linear discriminant analysis, kernel PCA, and neighborhood preserving embedding, are used to model the data structure. Then, Delaunay triangulation nets are used to build a discrete approximation of the geometrical structure of the unlabeled data cloud in a low-dimensional space. To show the effectiveness of this novel sampling strategy, results on three real multi-/hyperspectral data sets are presented, adding a thorough comparison with other state-of-the-art AL techniques. In comparison to conventional AL heuristics, the proposed techniques are able to obtain competitive or even better classification accuracy values.

Hyperspectral data classification problems have been extensively studied in the past decade. However, well designed features and a robust classifier are still open issues that impact on the performance of an automatic land-cover classification system. In this paper, we propose a deep feature represenation method that generates very good features and a classifier for pixel-wise hyperspectral data classification. The proposed method has two main steps: principle components of the hyperspectral image cube is first filtered by three dimensional Gabor wavelets; second, stacked autoencoders are trained on the outputs of the previous step through unsupervised pre-training, finally deep neural network is trained on those stacked autoencoders. Experimental results obtained on real hyperspectral image confirmed the effectiveness of the proposed approach in favors of the high classification accuracy and computation efficiency.

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.

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.

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.

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.

Relevant component analysis has shown effective in metric learning. It finds a transformation matrix of the feature space using equivalence constraints. This paper explores this idea for constructing a feature metric (FM) and develops a novel semisupervised feature-selection technique for hyperspectral image classification. Two feature measures referred to as band correlation metric (BCM) and band separability metric (BSM) are derived for the FM. The BCM can measure the spectral correlation among the bands, while the BSM can assess the class discrimination capability of a single band. The proposed feature-metric-based affinity propagation (AP) (FM-AP) technique utilizes exemplar-based clustering, i.e., AP, to group bands from original spectral channels with the FM. Experimental results are conducted on two hyperspectral images and show the advantages of the proposed technique over traditional feature-selection methods.

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.