Hyperspectral target detection (HTD) can provide detailed information about the objects and materials within a scene and holds significant importance in remote sensing image analysis. Traditional HTD methods often suffer from low detection accuracy when applied to hyperspectral images (HSIs) with low spatial resolution, where the target only occupies a few pixels. This can be addressed by exploiting the higher spatial resolution of multispectral images (MSIs). In addition, many cloud-based HTD methods, which rely on the distributed processing capability of cloud computing to cope with large-scale datasets, may result in long transmission delays that cannot meet real-time requirements. This article suggests a cloud-edge collaborative HTD approach based on the fusion of remotely sensed HSIs and MSIs. We first introduce an HTD algorithm that employs low-rank matrix decomposition and hierarchical constraint energy minimization (hCEM) to fuse a low-resolution HSI (LR-HIS) and a high-resolution MSI (HR-MSI). Aiming at a continuous shooting scenario, we further present a cloud-edge implementation of the HTD algorithm through the collaboration of a cloud cluster and edge servers deployed close to data acquisition devices. The overall processing flow of remotely sensed data fusion in the cloud-edge environment is formulated as a flowshop scheduling-like optimization problem. We develop a co-optimization scheduling algorithm to explore the best resource allocation solutions to the formulated problem. Experimental results on both general-purpose and real-world datasets show that the newly proposed HTD algorithm leads to significant improvements in detection accuracy over traditional methods, and the cloud-edge collaborative approach further enhances computational efficiency.

Global climate change leads to increasing frequency and severity of urban flood disasters, which restricts human sustainable development. So far, many studies have explored the potential of fusing remote sensing images and social media data for monitoring urban flood disasters. However, the SAR flooded characteristics of different land cover in complex urban environment are generally ignored. In this paper, we develop a new method for the fusion of heterogeneous SAR images and social media data by using domain adaptation and land cover information. The 2017 Houston flood event is taken as a case study for evaluation. According to our experiments, compared with traditional methods of optimal transport and geographic optimal transport, the proposed method can align the geo-tagged tweets with location uncertainty to nearby flooded areas of low-, medium-, and high-intensity developed areas with more remarkable performance.

Oriented object detection is an important research topic in remote sensing. The detection of oriented objects in remote sensing images remains a daunting challenge due to their complex backgrounds, various sizes, diverse aspect ratios, and especially arbitrary orientations. In recent years, keypoint-based anchor-free object detectors have demonstrated outstanding performance in this field. However, in current anchor-free detectors, object keypoints are primarily generated using the Gaussian kernel function, which assumes a circular form. This representation falls short in accurately conveying an object’s size and orientation. To address the aforementioned issue, this paper proposes a keypoint-based oriented object detector called MRSDet, which innovatively adopts the Tricube kernel, scales and rotates it, to better generate the center keypoint heatmap of the object. Besides, to improve the model’s detection performance on oriented objects and improve its ability to perceive object keypoints and boundary boxes, we also design a large receptive field mask module (LRFM), which is based on large convolution kernel decomposition and semantic segmentation masks. Taking the BBAVectors method as a baseline, we conduct experiments on multiple types of remote sensing datasets such as HRSC2016, UCAS-AOD and SSDD+ to verify the effectiveness and generalizability of the proposed method.

Constrained by the performance of edge devices and real time (RT) processing technology, the existing hyperspectral target detection algorithms often struggle to rapidly distinguish targets from complex background pixels during real-time detection. To address this issue, this article proposes a new real-time cloud-edge selective background energy constrained (CE-SBEC) hyperspectral target detection algorithm. This algorithm aims to obtain detection results in real-time after capturing new data. Moreover, it conducts in-depth analysis based on existing detection results and updates the algorithm’s internal data to enhance its capabilities in terms of global background annihilation (GBA) and complex background suppression (CBS). Consequently, it improves the accuracy of subsequent real-time detection results. To enhance the resource utilization, this article deploys various task nodes of the algorithm separately on both the cloud and the edge, enabling collaborative execution of the CE-SBEC algorithm. In our context, edge devices are airborne equipment designed for the rapid acquisition and processing of data at the site of data collection, while cloud computing devices refer to high-performance computing clusters situated at a significant distance from the data collection site. Experimental results demonstrate that compared with existing detection algorithms, our newly proposed method achieves more accurate detection results while ensuring real-time performance.

Hyperspectral image (HSI) super-resolution, which aims at improving the spatial quality of HSIs by fusing a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI), has drawn significant attention. Numerous LR-HSI and HR-MSI (HSI-MSI) fusion algorithms have emerged in recent times, yet they suffer from a lack of generality and integration, which hampers their usability for non-expert users. Moreover, these algorithms encounter significant challenges due to the exponential increase in remote sensing data volume. In this study, we propose a unified cloud-based framework for HSI-MSI fusion based on the general distributed alternating direction method of multipliers that incorporates nonlocal principles and tensor decomposition. The framework not only provides end-users with visualization modeling capabilities equipped with standard and comprehensive components, but also enhances the parallel processing capabilities of cloud computing. We employ a new proposed nonlocal adaptive low-rank coupled tensor canonical polyadic (CP) decomposition algorithm as a case study to evaluate the performance of this framework. Specifically, we establish the LR-HSIs and HR-MSIs relationship using order-4 coupled tensor CP decomposition and suggest an adaptive CP rank estimation method for achieving better super-resolution results. Experimental results on publicly available datasets demonstrate that the proposed parallel distributed optimization algorithm can achieve significant speedup with guaranteed accuracy. The proposed framework enables convenient and efficient processing of large-scale remote sensing data, effectively addressing the challenges associated with handling large data volumes. The source code of our method is released and available online at https://github.com/ZpWaitingForSunshine/DNAC4TCP/.

Hyperspectral target detection (HTD) aims to detect fine targets in hyperspectral images (HSIs). The traditional HTD method in low-resolution hyperspectral image (LR-HSI) is incapable of detecting small targets, clearly and precisely. Accordingly, in this paper, we propose a hyperspectral and multispectral image fusion target detection method based on cloud-edge collaboration. In this method, LR-HSI is first employed for coarse detection with the output of some suspicious target areas. Afterwards, the hyperspectral images and multispectral images (HSI-MSI) fusion is performed on these areas for precise target detection. In order to ensure the efficiency of HTD, we intend to accelerate our method in parallel based on the cloud-edge collaborative architecture. Furthermore, we establish an optimization model and design a greedy strategy to achieve the optimal deployment for minimizing the shortest runtime on the cloud-edge collaborative architecture. The experimental results demonstrate that our proposed method can significantly improve the computational efficiency while ensuring the accuracy.

With the recent development of remote sensing technology, large image repositories have been collected. In order to retrieve the desired images of massive remote sensing data sets effectively and efficiently, we propose a novel central cohesion gradual hashing (CCGH) mechanism for remote sensing image retrieval. First, we design a deep hashing model based on ResNet-18 which has a shallow architecture and extracts features of remote sensing imagery effectively and efficiently. Then, we propose a new training model by minimizing a central cohesion loss which guarantees that remote-sensing hash codes are as close to their hash code centers as possible. We also adopt a quantization loss which promotes that outputs are binary values. The combination of both loss functions produces highly discriminative hash codes. Finally, a gradual sign-like function is used to reduce quantization errors. By means of the aforementioned developments, our CCGH achieves state-of-the-art accuracy in the task of remote sensing image retrieval. Extensive experiments are conducted on two public remote sensing image data sets. The obtained results support the fact that our newly developed CCGH is competitive with other existing deep hashing methods.

Hyperspectral image super-resolution aims to fuse a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI) to obtain a high-resolution hyperspectral image (HR-HSI). Tensor-based methods have demonstrated their outstanding ability in constructing the relationship between the LR-HSI and the HR-MSI. This paper introduces a nonlocal hierarchical Tucker decomposition (HTD) model for hyperspectral and multispectral image (HSI-MSI) fusion. First, similar nonlocal patch tensors are clustered according to their similarity in the HR-MSI. Next, the spatial/spectral relationship between the LR-HSI and the HR-MSI is extracted through HTD. The alternating direction method of multipliers (ADMM) is employed to solve the proposed model. Furthermore, to overcome the high computational complexity of the model solver, we propose an efficient distributed and parallel method to accelerate the fusion process. Experimental results demonstrate that the proposed method not only substantially outperforms state-of-the-art HSI-MSI fusion methods, but also achieves a significant acceleration rate.

Hyperspectral unmixing aims at extracting pure spectral signatures and estimating their corresponding abundances at each pixel. Traditional unmixing algorithms consider end-member extraction and abundance estimation as two separate steps, and the completion of abundance estimation requires results from other endmember extraction algorithms. Considering that convolutional neural networks (CNNs) have powerful learning and data fitting capabilities, some techniques based on deep learning (DL) have been proposed in the literature. Most of them only utilize spectral information and neglect spatial information. In addition, existing unmixing methods based on DL usually extract the weight and output of a specific activation layer as endmembers and abundances, respectively. In our work, we exploit 3D convolution to propose a new 3D convolution unmixing network (3DCUN) for hyperspectral unmixing. Two types of real data, i.e., Samson and Jasper, are used to evaluate the performance of our proposed 3DCUN in endmember extraction and abundance estimation. The experimental results reflect that our proposed 3DCUN gets accurate results in estimating endmembers and abundances.

The performance of hyperspectral anomaly detection based on representation models is importantly related to the corresponding dictionary. A good dictionary can optimally model background to detect anomalies. To realize adaptively background reconstruction, this paper constructs global-local dictionaries for collaborative representation detector by using adaptive-shape (SA-CRD). Specifically, robust principal component analysis (RPCA) is used to separate background and anomalies preliminarily. Then adaptive-shape neighbor is adopted to build local dictionaries for robust background region, and the robust background region is clustered to construct a global dictionary for potential anomaly region. Finally, global-local dictionaries are used in the collaborative representation model to finish anomaly detection. Obtained results over two real data sets indicate that the proposed method can improve the accuracy of anomaly detection intensively compared to other state-of-art methods.

Convolutional neural networks (CNNs) have proven to be very efficient for the analysis of remote sensing (RS) images. Due to the inherent complexity of extracting features from these images, along with the increasing amount of data to be processed (and the diversity of applications), there is a clear tendency to develop and employ increasingly deep and complex CNNs. In this regard, graphics processing units (GPUs) are frequently used to optimize their execution, both for the training and inference stages, optimizing the performance of neural models through their many-core architecture. Hence, the efficient use of the GPU resources should be at the core of optimizations. This letter analyzes the possibilities of using a new family of CNNs, denoted as TResNets, to provide an efficient solution to the RS scene classification problem. Moreover, the considered models have been combined with mixed precision to enhance their training performance. Our experimental results, conducted over three publicly available RS data sets, show that the proposed networks achieve better accuracy and more efficient use of GPU resources than other state-of-the-art networks. Source code is available athttps://github.com/mhaut/GPUfriendlyRS.

Endmember estimation consists of two tasks, that is, determining the number of pure spectral constituents (endmembers) and extracting their spectral signatures. We present a new geometric distance-based method for endmember estimation from hyperspectral images (HSIs), which does not need to know the number of endmembers in advance. Our strategy optimizes the widely used maximum distance analysis (MDA) method from two viewpoints. First, the traditional MDA method performs endmember estimation by computing the maximum distances between any pixel and one specific pixel, line, plane, or affine hull (AH) composed by the endmembers that have been formerly extracted. Instead, our new strategy only requires computing the maximum distance between any pixel and one specific AH. This operation provides a simpler way than MDA to estimate endmembers. Second, our strategy exploits a new distance computation between any pixel and an AH and just needs the normal vector (compared to the traditional MDA method, which uses the normal vector and offset). The new distance computation in our method is much more efficient than that in the traditional MDA method.

Spectral unmixing plays a vital role in hyperspectral image analysis. It mainly consists of two procedures, i.e., endmember extraction and abundance estimation. Although most algorithms for each of the two procedures may exhibit good performance, few studies have been done considering both problems simultaneously. Therefore, hyperspectral unmixing accuracy is normally achieved by exploring all possible combinations of the two types of algorithms, which renders high computational overloads. We propose a novel orthogonal projection framework to conduct fast hyperspectral unmixing. It addresses both endmember extraction and abundance estimation with orthogonal projection endmember (OPE) and orthogonal projection abundance (OPA). Especially, the pixel with the largest orthogonal projection on any pixel is considered to be an endmember. We randomly choose one pixel from the hyperspectral data to compute the orthogonal projections of all pixels and extract the pixel with the largest projection as the first endmember. To avoid extracting the same endmembers, we compute orthogonal projections of all pixels to endmembers that have been previously extracted, and the pixel with the largest projection is considered as the next endmember. In terms of abundance estimation, we also utilize the concept of orthogonal projection and search for a diagonal matrix whose multiplication with the endmember matrix is not only a square matrix but also a diagonal matrix. Then, we exploit some specific matrix operations to estimate the abundance of each endmember at every pixel. We have evaluated the proposed OPE and OPA algorithms on synthetic and real data, and the experimental results have validated their effectiveness and efficiency in hyperspectral unmixing.

Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on collaborative representation model have attracted significant attention. Nevertheless, these methods have to face two main challenges: (1) all features (spectral signatures) are constrained to share the same representation coefficient, which ignores the differences among features; (2) existing dictionaries for pixel-by-pixel detection model are usually not reliable. To address these issues, this paper proposes a new relaxed collaborative representation detector for hyperspectral anomaly detection by using a novel non-global dictionary. The proposed detector conducts collaborative representation on each feature dimension of the pixel under test, and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The non-global dictionary is constructed by combining the k-nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual windows dictionary. In addition, this paper also designs a band selection strategy for the proposed method. Experiments on five real datasets indicate that the proposed method suppresses background well and outperforms other classical and state-of-the-art methods.

Social media data generated from individuals provides a unique opportunity to gain valuable insight on information flow, especially for emergency response. However, the inherent limitations associated to these data (particularly, the spatial bias) restrict its precise application. Existing research on spatial bias correction of social media data mainly face two issues: 1) the geographic extent in target domains may be underestimated, and 2) source elements may be transported within inappropriate distance. In this paper, we take 2013 Boulder, Colorado flood event as a case study, and present a new method called subspace optimal transport (SOT). Our proposed SOT aims at transporting biased tweets from dry to real flooded areas with a relatively close distance. Specifically, a comparison between our newly developed SOT and the traditional optimal transport (OT) and geographic optimal transport (GOT) is performed. Experimental results demonstrate that our new SOT method is able to correct the spatially biased geo-referenced tweets, with high precision and excellent computing performance.

Land cover classification of remotely sensed images is an extremely important and challenging task. During the last two decades, several methods have been proposed to deal with this problem. In particular, convolutional neural network (CNN)-based methods for land cover classification have enjoyed high popularity due to their strong feature extraction and characterization abilities. However, most CNNs-based methods use relatively small kernels (usually, 3 x 3 pixels in size). Increasing the size of the kernel introduces a lot of parameters and renders considerable computational overloads. To address this issue and allow for the processing of large image datasets, the pyramidal convolution (PyConv) network has been adopted. PyConv network contains several levels of kernels with varying scales and depths, and shows significant improvements in the task of visual recognition. In this paper, we evaluate the performance of the PyConv network on the UCMERCED dataset. Our experimental results reveal that the considered approach exhibits good performance and high efficiency in the task of land cover classification.

Convolutional neural networks (CNNs) have demonstrated excellent performance in hyperspectral image (HSI) classification. However, tuning some critical hyperparameters of a CNN-such as the receptive field (RF) size-presents a major challenge due to the presence of features with different scales in HSIs. Contrary to the conventional design of CNNs, which fixes the RF size, it has been proven that the RF size is modulated by the stimulus and hence, depends on the scene being considered. Such a dilemma has been rarely considered in CNN design. In this letter, a new multibranch selective kernel network (MSKNet) is introduced, in which the input image is convolved using different RF sizes to create multiple branches so that the effect of each branch is adjusted by an attention mechanism according to the input contrast. As a result, our newly developed MSKNet is capable of modeling different scales. Our experimental results, conducted on three widely used HSIs, reveal that the MSKNet can outperform state-of-the-art CNNs in the context of HSI classification problems. The source code of our newly developed MSKNet is available from: https://github.com/mhaut/MSKNet-HSI.

High-resolution digital surface models (DSMs) provide valuable height information about the Earth's surface, which can be successfully combined with other types of remotely sensed data in a wide range of applications. However, the acquisition of DSMs with high spatial resolution is extremely time-consuming and expensive with their estimation from a single optical image being an ill-possed problem. To overcome these limitations, this letter presents a new unpaired approach to obtain DSMs from optical images using deep learning techniques. Specifically, our new deep neural model is based on variational autoencoders (VAEs) and generative adversarial networks (GANs) to perform image-to-image translation, obtaining DSMs from optical images. Our newly proposed method has been tested in terms of photographic interpretation, reconstruction error, and classification accuracy using three well-known remotely sensed data sets with very high spatial resolution (obtained over Potsdam, Vaihingen, and Stockholm). Our experimental results demonstrate that the proposed approach obtains satisfactory reconstruction rates that allow enhancing the classification results for these images. The source code of our method is available from: https://github.com/mhaut/UIMG2DSM.

As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more specifically in remote sensing techniques and procedures, allowing for the acquisition of data sets with greater quality at higher acquisition ratios. This results in the collection of huge amounts of remotely sensed data, characterized by their large spatial resolution (in terms of the number of pixels per scene), and very high spectral dimensionality, with hundreds or even thousands of spectral bands. As a result, remote sensing instruments on spaceborne and airborne platforms are now generating data cubes with extremely high dimensionality, imposing several restrictions in terms of both processing runtimes and storage capacity. In this article, we provide a comprehensive review of the state of the art in DL for remote sensing data interpretation, analyzing the strengths and weaknesses of the most widely used techniques in the literature, as well as an exhaustive description of their parallel and distributed implementations (with a particular focus on those conducted using cloud computing systems). We also provide quantitative results, offering an assessment of a DL technique in a specific case study (source code available: https://github.com/mhaut/cloud-dnn-HSI). This article concludes with some remarks and hints about future challenges in the application of DL techniques to distributed remote sensing data interpretation problems. We emphasize the role of the cloud in providing a powerful architecture that is now able to manage vast amounts of remotely sensed data due to its implementation simplicity, low cost, and high efficiency compared to other parallel and distributed architectures, such as grid computing or dedicated clusters.

Hyperspectral imaging (HSI) is a competitive remote sensing technique in several fields, from Earth observation to health, robotic vision, and quality control. Each HSI scene contains hundreds of (narrow) contiguous spectral bands. The amount of data generated by HSI devices is often both a solution and a problem for a given application. Extracting information from HSI data cubes is a complex and computationally demanding problem. To tackle this challenge, convolutional neural networks (CNNs) have been widely applied to HSI classification. Despite their success, CNNs are computationally demanding algorithms with high memory requirements due to their large number of internal parameters. The recent interest in using HSI devices in mobile and embedded systems for air and spaceborne platforms turned the attention to computationally lightweight CNN architectures with good classification accuracy. In this article, we present a contribution in that direction. The proposed method combines the ghost-module architecture with a CNN-based HSI classifier to reduce the computational cost and, simultaneously, achieves an efficient classification method with high performance. Our new method is evaluated against nine standard HSI classifiers, and five improved deep-CNN architectures, over five commonly used HSI data sets for algorithm benchmarking. Conducted experiments show that the proposed method exhibits similar or better performance than the other classifiers, achieving top values in the considered performance metrics—even for very limited training sets—and, most importantly, with a fraction of the computational cost. Our novel approach for HSI classification is a strong candidate for implementation on systems with limited computational resources.

Convolutional neural networks (CNNs) have proven to be a powerful tool for the classification of hyperspectral images (HSIs). The CNN kernels are able to naturally include spatial information to smooth out the spectral variability and the noise present in HSI data. However, these kernels are composed of a large number of learning parameters that must be correctly adjusted to achieve good performance. This forces the model to consume a large amount of training data, being prone to overfitting when limited labeled samples are available. In addition, the execution of kernels is computationally very expensive, increasing quadratically with respect to the size of the convolution filter. This significantly reduces the performance of the model. To overcome the aforementioned limitations, this work presents a new few-parameter CNN (based on shift operations) for HSI classification that dramatically reduces both the number of parameters and the computational complexity of the model in terms of floating-point operations (FLOPs). The operational module combines a shift kernel (which adjusts the input data in particular directions without involving any parameters nor FLOPs) with pointwise convolutions that perform the feature extraction stage. The newly developed shift-based CNN has been employed to conduct HSI classification over five widely used and challenging data sets, achieving very promising results in terms of computational performance and classification accuracy.

Social media data have shown great potential for disaster response. However, the inherent limitations associated to these data (particu-larly, the spatial bias) restrict its precise application. In this work, we present a new spatial bias correction method based on remote sensing knowledge and spatio-temporal fusion, named locally optimal transport (LOT). Our method is first tested using a case study (2013 Boulder, Colorado flood event). Then, we apply our method to a 2016 Wuhan flood event to test its accuracy in a data deficient region. Our results show that combining remote sensing features and spatio-temporal fusion can help to address problems with a lack of prior data and limited disaster period data. According to the random ground verification points collected from news, pictures and videos, our new LOT method is able to accurately relocate spatially biased social media data to inundated areas, which are dangerous for users.

Hyperspectral imaging (HSI) gathers hundreds of images along the electromagnetic spectrum for the same area on the surface of the Earth, collecting a rich amount of spatial and spectral information. Deep learning classifiers have achieved significantly high precision results when analyzing HSI data. In particular, capsule networks (CapsNets) can provide robust classification results, overcoming the limitations of traditional convolutional neural networks (CNNs) by enriching the feature presentation capability and applying dynamic routing mechanisms. As a result, CapsNets are now widely regarded as the state-of-the-art within deep learning field. However, as it is the case for CNNs, the performance of CapsNets strongly depends on the quantity and quality of the available training samples, which in HSI tends to be scarce and noisy. Moreover, obtaining labeled data is expensive and time-consuming, and the high dimensionality of HSI data makes it difficult to accurately design classifiers based on limited training samples. This is mainly due to the strong intra-class variability present in the HSI data. Active learning (AL) can alleviate the aforementioned problems by selecting a small set of highly-representative labeled samples from a pool of unlabeled data, in iterative fashion. This paper presents a new AL-based approach for HSI data classification that integrates the spectral and the spatial information contained in the HSI data and enhances the performance of CapsNets when very limited training samples are available. Code: https://github.com/mhaut/AL-CapsNet-HSI.

In this paper, we describe a method for radiometric calibration of Fengyun-3D (FY-3D) MERSI-II TIR, bands 24 and 25 that combines vicarious and cross-calibration of the corresponding bands of Aqua MODIS, bands 31 and 32. A field campaign was conducted on lake Qinghai, China, on August 18, 2019. The surface measurements were performed before and after the passing time of Terra MODIS and FY-3D MERSI-II. Path radiance and transmittance were then calculated via the radiative transfer code MODTRAN 4.0, and the difference caused by the relative spectral response (RSR) of the sensors was eliminated by the spectral matching. Our experimental results indicate that the obtained radiometric calibration accuracy of FY-3D MERSI-II is stable during on-orbit operation.

High-resolution precipitation data are crucial to monitor disasters in urban areas, especially in cases with abundant precipitation. Based on the spatiotemporal, non-stationary relationship between precipitation and normalized differential vegetation index (NDVI), in this paper we introduce a geographically and temporally weighted regression (GTWR) model and further evaluate it in a case study in Guangdong province, China, in the summer of 2015. Our results indicate that there is a mainly negative correlation between precipitation and NDVI in the summer. Our GTWR downscaling model performs better than a previously available geographically weighted regression (GWR) model, providing more accurate downscaled precipitation estimations. This suggests that considering the spatiotemporal, non-stationarity relationship between NDVI and precipitation, our GTWR downscaling model can provide high-spatial resolution precipitation estimates, with more details in urban areas with abundant precipitation.

Deep neural networks (DNNs) exhibit great performance in the task of hyperspectral image (HSI) classification. However, these models are usually overparameterized and require large amounts of training data in order to properly avoid the curse of dimensionality and the variability of spectral signatures, thus suffering from overfitting problems when very few training samples are available, due to poor generalization ability in this particular case. The traditional regularization dropout (DO) strategy has been shown to be effective in fully connected DNNs but not in convolutional-based ones. This is mainly due to the way these architectures manage the spatial information. In this letter, we introduce a new approach to improve the generalization of convolutional-based models for HSI classification. Specifically, we develop a neighboring region DO technique that selectively cuts off certain neighboring outputs, creating spatial dropped regions. Our experimental results with two well-known HSIs reveal that the newly proposed method helps to achieve better classification accuracy than the traditional DO strategy, with a low computational cost.

Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this article considers deep learning models-such as convolutional neural networks (CNNs)-to perform spectral-spatial HSI denoising. The proposed model, called HSI single denoising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN.

Advances in deep learning (DL) have allowed for the development of more complex and powerful neural architectures. The adoption of deep convolutional-based architectures with residual learning [residual networks (ResNets)] has reached the state-of-the-art performance in hyperspectral image (HSI) classification. Traditionally, ResNets have been considered as stacks of discrete layers, where each one obtains a hidden state of the input data. This formulation must deal with very deep networks, which suffer from an important data degradation as they become deeper. Moreover, these complex models exhibit significant requirements in terms of memory due to the amount of parameters that need to be fine tuned. This leads to inadequate generalization and loss of accuracy. In order to address these issues, this article redesigns the ResNet as a continuous-time evolving model, where hidden representations (or states) are obtained with respect to time (understood as the depth of the network) through the evaluation of an ordinary differential equation (ODE), which is combined with a deep neural architecture. Our experimental results, conducted with four well-known HSI data sets, indicate that redefining deep networks as continuous systems through ODEs offers flexibility when processing and classifying these kinds of remotely sensed data, achieving significant performance even when a very few training samples are available.

This paper proposes a novel end-to-end learning model, called skip-connected covariance (SCCov) network, for remote sensing scene classification (RSSC). The innovative contribution of this paper is to embed two novel modules into the traditional convolutional neural network (CNN) model, i.e., skip connections and covariance pooling. The advantages of newly developed SCCov are twofold. First, by means of the skip connections, the multi-resolution feature maps produced by the CNN are combined together, which provides important benefits to address the presence of large-scale variance in RSSC data sets. Second, by using covariance pooling, we can fully exploit the second-order information contained in such multi-resolution feature maps. This allows the CNN to achieve more representative feature learning when dealing with RSSC problems. Experimental results, conducted using three large-scale benchmark data sets, demonstrate that our newly proposed SCCov network exhibits very competitive or superior classification performance when compared with the current state-of-the-art RSSC techniques, using a much lower amount of parameters. Specifically, our SCCov only needs 10% of the parameters used by its counterparts.

The probabilistic Latent Semantic Analysis (pLSA) model has recently shown a great potential to uncover highly descriptive semantic features from limited amounts of remote sensing data. Nonetheless, the high computational cost of this algorithm often constraints its operational application for land cover categorization tasks. In this scenario, this paper presents an Open Multi-Processing (OpenMP) implementation of the pLSA algorithm for unsupervised Synthetic Aperture Radar (SAR) and Multi-Spectral Imaging (MSI) image categorization. The experimental results suggest that multi-core systems are an important architecture for the efficient processing of both SAR and MSI datasets. Specifically, the proposed approach is able to cover a real scenario exhibiting good results in both accuracy and performance terms.

Deep neural networks (DNNs) have revolutionized the way remotely sensed hyperspectral image (HSI) data are managed and processed. For instance, residual networks (ResNets) have achieved high classification accuracy by applying sequential transformations (layer by layer) on the input HSI data, obtaining highly discriminative data representations. However, these models are quite complex, with significant requirements in terms of memory resulting from the large number of parameters that they need to learn, which also leads to potential overfitting issues. In this work, we specifically address the aforementioned problem by re-interpreting a DNN (the ResNet) as a continuous transformation, instead of the traditional (discrete) step-by-step approach. To achieve this, we combine ordinary differential equations (ODEs) with DNN architectures for the first time in the HSI data classification literature. This allows us to perform remotely sensed HSI data classification in an efficient way in terms of number of parameters. Our experimental results, conducted using two well-known HSI data sets, indicate that the inclusion of ODEs in the architecture of DNNs offers significant advantages when processing and classifying this kind of high-dimensional data, achieving better performance even with less training data.

Nonlinear unmixing of hyperspectral images has been a very challenging research problem, as it needs to consider the physical interactions between the sunlight scattered by multiple materials. In this paper, we propose a new approach for nonlinear unmixing which is based on multi-task learning (MTL) with low-rank matrix factorization (LRMF). The proposed approach establishes two tasks to conduct the unmixing problem under a nonlinear mixing model. In the first task, we employ LRMF to obtain endmember signatures and their corresponding abundance fractions simultaneously. Then, the second task uses LRMF to solve interactions from multiple scattering. The effectiveness of the proposed method is verified by using real hyperspectral data. Compared with other state-of-the-art nonlinear unmixing algorithms, the proposed approach demonstrates very competitive performance.

Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically limit their application in on-board remote sensing scenarios. In this context, low-power consumption architectures are promising platforms that may provide acceptable on-board computing capabilities to achieve satisfactory classification results with reduced energy demand. For instance, the new NVIDIA Jetson Tegra TX2 device is an efficient solution for on-board processing applications using deep-learning (DL) approaches. So far, very few efforts have been devoted to exploiting this or other similar computing platforms in on-board remote sensing procedures. This letter explores the use of low-power consumption architectures and DL algorithms for HSI classification. The conducted experimental study reveals that the NVIDIA Jetson Tegra TX2 device offers a good choice in terms of performance, cost, and energy consumption for on-board HSI classification tasks.

This letter introduces a novel remote sensing singleimage superresolution (SR) architecture based on a deep efficient compendium model. The current deep learning-based SR trend stands for using deeper networks to improve the performance. However, this practice often results in the degradation of visual results. To address this issue, the proposed approach harmonizes several different improvements on the network design to achieve state-of-the-art performance when superresolving remote sensing imagery. On the one hand, the proposal combines residual units and skip connections to extract more informative features on both local and global image areas. On the other hand, it makes use of parallelized 1×1 convolutional filters (network in network) to reconstruct the superresolved result while reducing the information loss through the network. Our experiments, conducted using seven different SR methods over the well-known UC Merced remote sensing data set, and two additional GaoFen-2 test images, show that the proposed model is able to provide competitive advantages.

Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy.However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy.In order to mitigate this problem, in this letter we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting.Our results with two well-known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost.

The current trend in remote sensing image superresolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery. Nonetheless, the inherent complexity of these architectures/data often makes these methods very difficult to train. Despite these recent advances, the huge amount of network parameters that must be fine-tuned and the lack of suitable high-resolution remotely sensed imagery in actual operational scenarios still raise some important challenges that may become relevant limitations in the existent earth observation data production environments. To address these problems, we propose a new remote sensing SR approach that integrates a visual attention mechanism within a residual-based network design in order to allow the SR process to focus on those features extracted from land-cover components that require more computations to be superresolved. As a result, the network training process is significantly improved because it aims at learning the most relevant high-frequency information while the proposed architecture allows neglecting the low-frequency features extracted from spatially uninformative earth surface areas by means of several levels of skip connections. Our experimental assessment, conducted using the University of California at Merced and GaoFen-2 remote sensing image collections, three scaling factors, and eight different SR methods, demonstrates that our newly proposed approach exhibits competitive performance in the task of superresolving remotely sensed imagery.

Advances in remote sensing hardware have led to a significantly increased capability for high-quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed. An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission. For instance, hyperspectral images (HSIs) are characterized by hundreds of spectral bands. In this sense, high-performance computing (HPC) and high-throughput computing (HTC) offer interesting alternatives. Particularly, distributed solutions based on cloud computing can manage and store huge amounts of data in fault-tolerant environments, by interconnecting distributed computing nodes so that no specialized hardware is needed. This strategy greatly reduces the processing costs, making the processing of high volumes of remotely sensed data a natural and even cheap solution. In this paper, we present a new cloud-based technique for spectral analysis and compression of HSIs. Specifically, we develop a cloud implementation of a popular deep neural network for non-linear data compression, known as autoencoder (AE). Apache Spark serves as the backbone of our cloud computing environment by connecting the available processing nodes using a master-slave architecture. Our newly developed approach has been tested using two widely available HSI data sets. Experimental results indicate that cloud computing architectures offer an adequate solution for managing big remotely sensed data sets.

Deep neural networks (DNNs), including convolutional neural networks (CNNs) and residual networks (ResNets) models, are able to learn abstract representations from the input data by considering a deep hierarchy of layers that perform advanced feature extraction. The combination of these models with visual attention techniques can assist with the identification of the most representative parts of the data from a visual standpoint, obtained through more detailed filtering of the features extracted by the operational layers of the network. This is of significant interest for analyzing remotely sensed hyperspectral images (HSIs), characterized by their very high spectral dimensionality. However, few efforts have been conducted in the literature in order to adapt visual attention methods to remotely sensed HSI data analysis. In this paper, we introduce a new visual attention-driven technique for the HSI classification. Specifically, we incorporate attention mechanisms to a ResNet in order to better characterize the spectral-spatial information contained in the data. Our newly proposed method calculates a mask that is applied to the features obtained by the network in order to identify the most desirable ones for classification purposes. Our experiments, conducted using four widely used HSI data sets, reveal that the proposed deep attention model provides competitive advantages in terms of classification accuracy when compared to other state-of-the-art methods.

The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a result, exploiting both spatial and spectral information is very important for HSI classification. This paper proposes a new hand-crafted feature extraction method, based on multiscale covariance maps (MCMs), that is specifically aimed at improving the classification of HSIs using CNNs. The proposed method has the following distinctive advantages. First, with the use of covariance maps, the spatial and spectral information of the HSI can be jointly exploited. Each entry in the covariance map stands for the covariance between two different spectral bands within a local spatial window, which can absorb and integrate the two kinds of information (spatial and spectral) in a natural way. Second, by means of our multiscale strategy, each sample can be enhanced with spatial information from different scales, increasing the information conveyed by training samples significantly. To verify the effectiveness of our proposed method, we conduct comprehensive experiments on three widely used hyperspectral data sets, using a classical 2-D CNN (2DCNN) model. Our experimental results demonstrate that the proposed method can indeed increase the robustness of the CNN model. Moreover, the proposed MCMs+2DCNN method exhibits better classification performance than other CNN-based classification strategies and several standard techniques for spectral-spatial classification of HSIs.

Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral-spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral-spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton's capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral-spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral-spatial capsules, which are able to learn HSI spectral-spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral-spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral-spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral-spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral-spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods.

The large amount of data produced by satellites and airborne remote sensing instruments has posed important challenges to efficient and scalable processing of remotely sensed data in the context of various applications. In this paper, we propose a new big data framework for processing massive amounts of remote sensing images on cloud computing platforms. In addition to taking advantage of the parallel processing abilities of cloud computing to cope with large-scale remote sensing data, this framework incorporates task scheduling strategy to further exploit the parallelism during the distributed processing stage. Using a computation- and data-intensive pan-sharpening method as a study case, the proposed approach starts by profiling a remote sensing application and characterizing it into a directed acyclic graph (DAG). With the obtained DAG representing the application, we further develop an optimization framework that incorporates the distributed computing mechanism and task scheduling strategy to minimize the total execution time. By determining an optimized solution of task partitioning and task assignments, high utilization of cloud computing resources and accordingly a significant speedup can be achieved for remote sensing data processing. Experimental results demonstrate that the proposed framework achieves promising results in terms of execution time as compared with the traditional (serial) processing approach. Our results also show that the proposed approach is scalable with regard to the increasing scale of remote sensing data.

The relatively recent availability of operational products from Sentinel-2 and Sentinel-3 missions gives widespread opportunities to combine data collected from different sensors in order to provide products of a higher processing level. Nonetheless, the availability of these products may be affected by multiple factors, such as cloud occlusions, band saturation, geolocation errors or even misaligned detectors. All these anomalies affecting remote sensing data may eventually limit the accessibility to fused products because some of the required information may become partially unavailable for specific areas of interest. In this scenario, the work presented here aims at analyzing the effectiveness of several state-of-the-art regression models in order to restore Sentinel-3 products with partial anomalies from Sentinel-2 integral data. In particular this work investigates three regression methods, two linear-regression method and a non-linear artificial neural networks based method. Obtained results prove that the nonlinear approach and linear RIDGE method are able to carry out a good estimation of S3 from S2 data.

During recent years, many regularization techniques have been proposed to deal with ill-posed problems related to hyperspectral image classification, in which the limited number of training samples contrasts with the very high spectral dimensionality. However, the intrinsic structure of a hyperspectral image often depends on the specific scene and spectrometer, although regularizers like Ridge, LASSO, etc, have been widely used in practical applications. Instead of imposing these regularizers to the probabilistic output of a classifier, this work evaluates the use of extreme learning machines (ELM) with output weights of a single-hidden layer feed-forward neural network (SLFN) regularized with Ridge and LASSO priors, respectively. Experimental results with several real hyperspectral images are conducted to compare the performance and adaptation of these two regularizers with the the original ELM in classification scenarios.

Computational advances have allowed for the development of deep learning (DL) applied to remote sensing data and, particularly, to hyperspectral image (HSI) classification. Deeper architectures are able to establish a better separation of the characteristics of the data, allowing for a better and accurate performance. However, it is known that employing very deep architectures with many abstraction levels can result in a loss of information due to the fact that deep networks often normalize each data individually, without considering the set of adjacent data. To address this issue, this paper implements a self-normalizing neural network (SNN) in order to extract high-level abstract representations without losing information due to the data initialization. The selected activation function (scaled exponential linear units or SELU) normalizes the data considering their neighborhood's information and a special dropout technique (a-dropout), obtaining good classification performance while maintaining the data characteristics across the successive layers. Obtained results show that the proposal improves the performance with few training samples.

Solving the mixture problem in remotely sensed hyperspectral images remains a challenging task. In particular, solutions are needed in order to obtain a response for applications with real-time constraints. In the last decade, several efforts have been developed, many of them using graphics processing units (GPUs) and focused on the exploitation of spectral information alone. However, a few spectral unmixing chains have been developed using other architectures such as multicore processors, field programmable gate arrays, or Intel Xeon Phi coprocessors. In this letter, we develop a new parallel unmixing chain for multicore processors. Compared with other approaches, the proposed spatial-spectral alternative takes advantage of the complementary information provided by the spatial correlation of the pixels in the image in addition to the spectral information. Our implementation has been optimized using the application program interface OpenMP and the Intel Math Kernel Library on two multicore architectures, and using real analysis scenarios. The results reveal competitive real-time performance compared with another compute unified device architecture implementation previously developed for GPUs.

Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover categorization. Then, a novel pLSA-based image fusion approach is presented, which pursues to uncover multimodal feature patterns from SAR and MSI data in order to effectively fuse and categorize Sentinel-1 and Sentinel-2 remotely sensed data. Experiments conducted over two different data sets reveal the advantages of the proposed approach for unsupervised land-cover categorization tasks.

As an unsupervised learning tool, autoencoder has been widely applied in many fields. In this letter, we propose a new robust unmixing algorithm that is based on stacked nonnegative sparse autoencoders (NNSAEs) for hyperspectral data with outliers and low signal-to-noise ratio. The proposed stacked autoencoders network contains two main steps. In the first step, a series of NNSAE is used to detect the outliers in the data. In the second step, a final autoencoder is performed for unmixing to achieve the endmember signatures and abundance fractions. By taking advantage from nonnegative sparse autoencoding, the proposed approach can well tackle problems with outliers and low noise-signal ratio. The effectiveness of the proposed method is evaluated on both synthetic and real hyperspectral data. In comparison with other unmixing methods, the proposed approach demonstrates competitive performance.

In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial-contextual information in the scene when conducting feature extraction (FE) from hyperspectral images (HSIs). Specifically, our method first projects the HSI into a subspace, using the maximum noise fraction method. Then, for each test pixel in the subspace, its most similar neighboring pixels (within a local spatial window) are clustered using the cosine distance measurement. The test pixel and its neighbors are used to calculate a local CM for FE purposes. Each nondiagonal entry in the matrix characterizes the correlation between different spectral bands. Finally, these matrices are used as spatial-spectral features and fed to a support vector machine for classification purposes. The proposed method offers a new strategy to characterize the spatial-spectral information in the HSI prior to classification. Experimental results have been conducted using three publicly available hyperspectral data sets for classification, indicating that the proposed method can outperform several state-of-the-art techniques, especially when the training samples available are limited.

This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches.

Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details than those captured by the original acquisition instrument. Therefore, SR techniques are particularly useful to cope with the increasing demand remote sensing imaging applications requiring fine spatial resolution. Even though different machine learning paradigms have been successfully applied in SR, more research is required to improve the SR process without the need of external high-resolution (HR) training examples. This paper proposes a new convolutional generator model to super-resolve low-resolution (LR) remote sensing data from an unsupervised perspective. That is, the proposed generative network is able to initially learn relationships between the LR and HR domains throughout several convolutional, downsampling, batch normalization, and activation layers. Then, the data are symmetrically projected to the target resolution while guaranteeing a reconstruction constraint over the LR input image. An experimental comparison is conducted using 12 different unsupervised SR methods over different test images. Our experiments reveal the potential of the proposed approach to improve the resolution of remote sensing imagery.

Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification.

This paper proposes a new method, called multilayer stacked covariance pooling (MSCP), for remote sensing scene classification. The innovative contribution of the proposed method is that it is able to naturally combine multilayer feature maps, obtained by pretrained convolutional neural network (CNN) models. Specifically, the proposed MSCP-based classification framework consists of the following three steps. First, a pretrained CNN model is used to extract multilayer feature maps. Then, the feature maps are stacked together, and a covariance matrix is calculated for the stacked features. Each entry of the resulting covariance matrix stands for the covariance of two different feature maps, which provides a natural and innovative way to exploit the complementary information provided by feature maps coming from different layers. Finally, the extracted covariance matrices are used as features for classification by a support vector machine. The experimental results, conducted on three challenging data sets, demonstrate that the proposed MSCP method can not only consistently outperform the corresponding single-layer model but also achieve better classification performance than other pretrained CNN-based scene classification methods.

Accurately estimating the elements in Earth observations is crucial when assessing specific features such as air quality index, water pollution, or urbanization process behavior. Moreover, physical-chemical composition can be retrieved from hyperspectral images when proper spectral unmixing architectures are employed. Specifically, when linear and nonlinear combinations of endmembers (pure spectral components) are accurately characterized, hyperspectral unmixing plays a key role in understanding and quantifying phenomena occurring over the instantaneous field-of-view. Thus, reliable detection of nonlinear reflectance behavior can play a key role in enhancing hyperspectral unmixing performance. In this paper, two new methods for adaptive design of mixture models for hyperspectral unmixing are introduced. One of the methods relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers' spectra. Then, an iterative process aims at understanding the order of local nonlinearity that is displayed by each endmember over every pixel. An improved version of an artificial neural network-based approach for nonlinearity order information is also considered and compared. Experimental results show that the proposed approaches are actually able to retrieve thorough information on the nature of the nonlinear effects over the image, while providing excellent performance in reconstructing the given data sets.

Artificial neural networks (ANNs) have been widely used in the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention. Unlike traditional CNNs methods, where the relevant information to classify the elements of a remotely sensed image is extracted only from the last fully-connected layer, the new adaptive deep pyramid matching (ADPM) model [1] takes advantage of the features from all of the convolutional layers. This model allows the optimal fusing weights for different convolutional layers be learned from the data itself. In addition, the combination of CNNs with spatial pyramid pooling (SPP-net) to create the basic deep network allows the use of images with multiple scales, which results in better learning process thanks to the complementary information. The original ADPM method is divided in two parts: the multi-scale deep feature extraction and the ADPM core. In this paper we present a computational improvement of the ADPM core, coding a parallel-multicore version. This strategy is shown to significantly enhance performance in the analysis of remotely sensed data.

The finer spatial, spectral and radiometric resolutions of current and planned sensors are rendering increasingly-high data rates which, coupled with limited on-board storage, downlink bandwidth and receiving ground station availability, make high-throughput, high-performance data-reduction techniques essential in forthcoming missions. On this paper we describe an algorithm well suited to high-dimensional data as those produced by multispectral and hyperspectral sensors, both highly relevant in a broad range of Earth Observation activities with the latter becoming increasingly available and delivering the highest data rates. The performance of parallel implementations of the algorithm on multi-core and GPU architectures is also evaluated.

Sparse unmixing of hyperspectral data is an important technique which aims at estimating the fractional abundances of endmembers (pure spectral components). It is well known that enforcing sparseness becomes a necessary process in sparse unmixing methods. To better exploit the sparsity in hyperspectral imagery, a double reweighted sparse unmixing algorithm has been proposed. However, it focusses on analyzing the hyperspectral data without fully incorporating the spatial information. To address this limitation, a spatial weighted sparse unmixing (SWSU) algorithm is proposed in this paper, which can take full advantage of the spatial information and further enhance the sparsity of the abundances. This is done by incorporating local neighborhood weights into the double reweighted sparse unmixing formulation. Experimental results on simulated hyperspectral data sets illustrate the good potential of the spatial weighted strategy for sparse unmixing introduced in this paper, which can greatly improve abundance estimation results.

Morphological attribute profiles (MAPs) are one of the most effective methodologies to characterize the spatial information in remote sensing images. This technique extracts components able to accurately describe objects in the surface of the Earth. In this work, we present a new method for impervious surface extraction from multispectral images using morphological attribute profiles. The proposed method first uses morphological profiles to extend Landsat ETM+ images with additional features. Then, we adopt a vegetation-impervious surface-soil (V-I-S) model and extract three pure classes (endmembers) from these images (i.e. vegetation, impervious surface and soil) using the vertex component algorithm (VCA). Finally, linear spectral mixture analysis (SMA) is conducted to extract the impervious surface percentage (ISP). To test the performance of the proposed method, more than 300 test samples including business districts, residential areas and urban roads are randomly selected from QuickBird imagery with very high resolution. The coefficient of determination R2is 0.7571, which significantly outperformed other standard techniques in the literature. The obtained experimental results demonstrate that the proposed approach based on morphological attribute profiles can lead to very good extraction and characterization of impervious surfaces.

Remote sensing is a powerful technology for Earth observation (EO), and it plays an essential role in many applications, including environmental monitoring, precision agriculture, resource managing, urban characterization, disaster and emergency response, etc. However, due to limitations in the spectral, spatial, and temporal resolution of EO sensors, there are many situations in which remote sensing data cannot be fully exploited, particularly in the context of emergency response (i.e., applications in which real/near-real-time response is needed). Recently, with the rapid development and availability of social media data, new opportunities have become available to complement and fill the gaps in remote sensing data for emergency response. In this paper, we provide an overview on the integration of social media and remote sensing in time-critical applications. First, we revisit the most recent advances in the integration of social media and remote sensing data. Then, we describe several practical case studies and examples addressing the use of social media data to improve remote sensing data and/or techniques for emergency response.

The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available.

The use of spatial information prior to spectral unmixing of hyperspectral data is a very active research line in recent years. There are many approximations that consider spatial characteristics of the data in order to guide the endmember identification/extraction procedure. In particular, the spatial preprocessing (SPP) algorithm can be used prior to most existing spectral-based endmember identification techniques, thus promoting the selection of endmembers in spatially representative parts of the scene. The main concern regarding SPP and this kind of preprocessing techniques is that they are computational expensive, adding a significant burden to the spectral unmixing process which should be alleviated. In this paper we revisit and enhance a previously developed implementation of SPP for graphical processing units (GPUs) in order to increase its performance by exhaustively using the level one (L1)-cache level of the GPU. The performance of the proposed implementation is evaluated using an NVidiaTMGeForce GTX 580. Our experimental validation reveals that real-time processing performance can be obtained for real hyperspectral data sets collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).

Linear spectral unmixing consists on the identification of spectrally pure constituents, called endmembers and their corresponding proportions or abundances using a linear model. Traditionally, most of the attention has been focussed on the exploitation of spectral information when identifying a set of endmembers and, only recently, some techniques try to take advantage of complementary information such as the one provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in most of these spatial-spectral based techniques, as hyperspectral images provide very rich information in both the spatial and the spectral domain. In this paper we provide a computationally efficient implementation of a spatial-spectral processing (SSPP) algorithm which can be used prior to endmember identification and spectral unmixing. Specifically we present an implementation optimized for commodity graphics processing units (GPUs), which is evaluated using two different GPU architectures from NVidia: GeForce GTX580 and GeForce GT740. Our experimental validation reveals that significant speedups can be achieved when processing hyperspectral images of different sizes.

Spectral unmixing pursues the identification of spectrally pure constituents, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in these spatial-spectral techniques, as hyperspectral images contain very rich information in both the spatial and spectral domains. In this letter, we develop a computationally efficient implementation of a spatial-spectral processing algorithm that has been successfully applied prior to the spectral unmixing of the hyperspectral data. Our implementation has been optimized for the commodity graphics processing units (GPUs) and is evaluated (using both synthetic and real data) using different GPU architectures. Significant speedups can be achieved when processing hyperspectral images of different sizes. This allows for the inclusion of the proposed parallel preprocessing module in a full hyperspectral unmixing chain able to operate in real time.

The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics of the hyperspectral data. Particularly, spatial preprocessing algorithm (SPP) consists on a preprocessing technique that can be used prior to most of existing spectral-based endmember extraction process, thus promoting the selection of endmem-bers from the most spatially homogeneous regions of the data set. This paper presents a parallel implementation of SPP algorithm which is tested over two different graphic processing units (GPUs) architectures: NVidiaTMGeForce GTX 580 and NVidiaTMGeForce GTX 870M. Experimental validation using a hyperspectral data set collected by AVIRIS sensor shows that it is possible to achieve real-time performance.

We describe the minimum volume simplex enclosure problem (MVSEP), which is known to be a global optimization problem, and further investigate its multimodality. The problem is a basis for several (unmixing) methods that estimate so-called endmembers and fractional values in a linear mixing model. We describe one of the estimation methods based on MVSEP. We show numerically that using nonlinear optimization local search leads to the estimation results aimed at. This is done using examples, designing instances and comparing the outcomes with a maximum volume enclosing simplex approach which is used frequently in unmixing data.

Spectral unmixing is an important technique for hyperspectral data exploitation, in which a mixed spectral signature is decomposed into a collection of spectrally pure constituent spectra, called endmembers, and a set of correspondent fractions, or abundances, that indicate the proportion of each endmember present in the mixture. Over the last years, several algorithms have been developed for automatic or semiautomatic endmember extraction. Some available approaches assume that the input data set contains at least one pure spectral signature for each distinct material and further conduct a search for the most spectrally pure signatures in the high-dimensional space spanned by the hyperspectral data. Among these approaches, those aimed at maximizing the volume of the simplex that can be formed using available spectral signatures have found wide acceptance. However, the presence of spectrally pure constituents is unlikely in remotely sensed hyperspectral scenes due to spatial resolution, mixing phenomena, and other considerations. In order to address this issue, other available algorithms have been developed to generate virtual endmembers (not necessarily present among the input data samples) by finding the simplex with minimum volume that encloses all available observations. In this paper, we discuss maximum-volume versus minimum-volume enclosing solutions and further develop a novel algorithm in the latter category which incorporates the fractional abundance estimation as an internal step of the endmember searching process (i.e., it does not require an external method to produce endmember fractional abundances). The method is based on iteratively enclosing the observations in a lower dimensional space and removing observations that are most likely not to be enclosed by the simplex of the endmembers to be estimated. The performance of the algorithm is investigated and compared to that of other algorithms (with and without the pure pixel assumption) using synthetic and real hyperspectral data sets collected by a variety of hyperspectral imaging instruments.

Hyper spectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have explored the application of morphological operations to integrate both spatial and spectral responses in hyper spectral data analysis. These operations rely on ordering pixel vectors in spectral space, but there is no unambiguous means of defining the minimum and maximum values between two vectors of more than one dimension. Our original contribution in this paper is to examine the impact of different vector ordering strategies on the definition of multi-channel morphological operations. Our focus is on morphological unmixing, which decomposes each pixel vector in the hyper spectral scene into a combination of pure spectral signatures (called end members) and their associated abundance fractions, allowing sub-pixel characterization. Experiments are conducted using real hyper spectral data sets collected by NASA/JPL's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system.

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral constituents (endmembers), and 2) end member abundance estimation in mixed pixels. One of the main problems concerning the identification of spectral endmembers is the lack of pure spectral signatures in real hyperspectral data due to spatial resolution and mixture phenomena happening at different scales. In this paper, we present a new method for endmember estimation which does not assume the presence of pure pixels in the input data. The method minimizes the volume of an enclosing simplex in the reduced space while estimating the fractional abundance of vertices in simultaneous fashion, as opposed to other volume-based approaches such as N-FINDR which inflate the simplex of maximumvolume that can be formed using available image pixels. Our experimental results and comparisons to other endmember extraction algorithms indicate promising performance of the method in the task of extracting endmembers from real hyperspectral data. In our experiments, we use laboratory-simulated forest scenes with known endmembers and fractional abundances due to their acquisition in a controlled environment using a real hyperspectral imaging instrument.

Abstract The purpose of content‐based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by latest‐generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever‐growing data archives. To address this need, this paper develops a new parallel CBIR system that has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub‐pixel precision using spectral mixture analysis concepts, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations and a Beowulf cluster at NASA's Goddard Space Flight Center. Our experimental results indicate that the proposed parallel system can efficiently retrieve hyperspectral images from complex image databases by efficiently adapting to the underlying parallel platform on which it is run, regardless of the heterogeneity in the compute nodes and communication links that form such parallel platform. Copyright © 2009 John Wiley & Sons, Ltd.

In this letter, we address the use of artificial neural networks for spectral mixture analysis of hyperspectral scenes. We specifically focus on the issue of how to effectively train neural network architectures in the context of spectral mixture analysis applications. To address this issue, a multilayer perceptron neural architecture is combined with techniques for intelligent selection and labeling of training samples directly obtained from the input data, thus maximizing the information that can be obtained from those samples while reducing the need fora prioriinformation about the scene. The proposed approach is compared to unconstrained and fully constrained linear mixture models using hyperspectral data sets acquired (in the laboratory) from artificial forest scenes, using the compact airborne spectrographic imaging system. The Spreading of Photons for Radiation INTerception (SPRINT) canopy model, which assumes detailed knowledge about object geometry, was employed to evaluate the results obtained by the different methods. Our results show that the proposed approach, when trained with both pure and mixed training samples (generated automatically withoutpriorinformation) can provide similar results to those provided by SPRINT, using very few labeled training samples. An application to real airborne data using a set of hyperspectral images collected at different altitudes by the digital airborne imaging spectrometer 7915 and the reflective optics system imaging spectrometer, operating simultaneously at multiple spatial resolutions, is also presented and discussed.

We develop a new approach for characterization of mixed pixels in remotely sensed hyperspectral images. The proposed method first performs joint spatial-spectral pixel characterization via extended morphological transformations, and then automatically extracts pure spectral signatures (called endmembers) using volume optimization and convex geometry concepts. The proposed method outperforms other widely used approaches in the analysis of a real hyperspectral scene collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada. Ground-truth information available from U.S. Geological Survey is used to substantiate our findings.

We have explored in this paper a framework to test in a quantitative manner the stability of different endmember extraction and spectral unmixing algorithms based on the concept of Consensus Clustering. The idea is to investigate if the sensibility of those algorithms to the number of endmembers can be used to estimate this parameter itself. Preliminary results on synthetic data reveal that the proposed scheme, which can be implemented efficiently in parallel, can compete with state-of-the-art schemes.

In previous work, we have reported that the scalability of parallel processing algorithms for hyperspectral image analysis is affected by the amount of data to exchanged through the communication network of the parallel system. However, large messages are common in hyperspectral imaging applications since processing algorithms are often pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of data compression techniques for improving the scalability of a standard spectral unmixin-based parallel hyperspectral processing chain on heterogeneous networks of workstations. Our experimental results indicate that adaptive, wavelet-based lossy compression can lead to improvements in the scalability of the parallel algorithms without significantly sacrificing algorithm analysis accuracy.

Spectral mixture analysis is an important task for remotely sensed hyperspectral data interpretation. In spectral unmixing, both the determination of spectrally pure signatures (endmembers) and the unmixing process that interprets mixed pixels as combinations of endmembers are computationally expensive procedures. An exciting recent development in the field of commodity computing is the emergence of programmable graphics processing units (GPUs), which are now increasingly being used address the ever-growing computational requirements introduced by hyperspectral imaging applications. In this paper, we develop three new GPU-based implementations of endmember extraction algorithms: the pixel purity index (PPI), a kernel version of the PPI (KPPI), and the automatic morphological endmember extraction (AMEE) algorithm. We also provide a GPU-based implementation of the fully constrained linear spectral unmixing algorithm.

Hyperspectral image processing has been a very active area in remote sensing and other application domains in recent years. Despite the availability of a wide range of advanced processing techniques for hyperspectral data analysis, many techniques for hyperspectral data classification are based on the consideration of spectral information separately from spatial information information, and thus the two types of information are not treated simultaneously. In this paper, we develop a new technique for joint spatial-spectral classification of hyperspectral image data which makes use of opening and closing by reconstruction, a kind of mathematical morphology operations which are extended here to hyperspectral images. A high performance parallel implementation of the proposed technique is also developed to satisfy time-critical constraints in remote sensing applications, using NASA's Thunderhead Beowulf cluster computer for demonstration purposes.

Many available techniques for spectral mixture analysis involve the separation of mixed pixel spectra collected by imaging spectrometers into pure component (endmember) spectra, and the estimation of abundance values for each end- member. Although linear mixing models generally provide a good abstraction of the mixing process, several naturally occurring situations exist where nonlinear models may provide the most accurate assessment of endmember abundance. In this paper, we propose a combined linear/nonlinear mixture model which makes use of linear mixture analysis to provide an initial model estimation, which is then thoroughly refined using a multi-layer neural network coupled with intelligent algorithms for automatic selection of training samples. Three different algorithms for automatic selection of training samples, such as border training algorithm (BTA), mixed signature algorithm (MSA) and mophological erosion algorithm (MEA) are developed for this purpose. The proposed model is evaluated in the context of a real application which involves the use of hyperspectral data sets, collected by the Digital Airborne (DAIS 7915) and Reflective Optics System (ROSIS) imaging spectrometers of DLR, operating simultaneously at multiple spatial resolutions.

Heterogeneous networks of workstations have rapidly become a cost-effective computing solution in many application areas. This paper develops several highly innovative parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in remote sensing-based homeland security and defense applications. In order to illustrate parallel performance, we consider four (partially and fully) heterogeneous networks of workstations distributed among different locations at University of Maryland, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results indicate that heterogeneous networks can be used as a viable low-cost alternative to homogeneous parallel systems in many on-going and planned remote sensing missions

The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice in Earth and planetary missions. In this paper, we develop a new morphological/neural parallel algorithm for commodity cluster-based analysis of high-dimensional remotely sensed image data sets. The algorithms accuracy and parallel performance are tested (in the context of a real precision agriculture application) using two parallel platforms: a fully heterogeneous cluster made up of 16 workstations at University of Maryland, and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center

Hyperspectral imaging is a new technique which has become increasingly important in many remote sensing applications, including automatic target recognition for military and defense/security deployment, risk/hazard prevention and response including wild land fire tracking, biological threat detection, monitoring of oil spills and other types of chemical contamination, etc. Hyperspectral imaging applications generate massive volumes of data and require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. Although most currently available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of workstations represent a very promising cost-effective solution expected to play a major role in the design of highperformance computing platforms for many on-going and planned remote sensing missions. This paper explores innovative techniques for mapping hyperspectral analysis algorithms onto heterogeneous networks of workstations available at NASA’s Goddard Space Flight Center and University of Maryland. Experimental results reveal that heterogeneous networks of workstations represent a source of computational power that is both accessible and applicable in hyperspectral imaging studies.

High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature. With the recent explosion in the amount and dimensionality of hyperspectral imagery, parallel processing is expected to become a requirement in most remote sensing missions. In this paper, we take a necessary first step towards the quantitative comparison of parallel techniques and strategies for analyzing hyperspectral data sets. Our focus is on three types of algorithms: automatic target recognition, spectral mixture analysis and data compression. Three types of high performance computing platforms are used for demonstration purposes, including commodity cluster-based systems, heterogeneous networks of distributed workstations and hardware-based computer architectures. Combined, these parts deliver a snapshot of the state of the art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel computing models into hyperspectral remote sensing problems.

The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. The price paid for such a wealth of spectral information available from latest-generation sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models in remote sensing missions. This paper explores three HPC-based paradigms for efficient information extraction from remote sensing data using the Pixel Purity Index (PPI) algorithm (available from the popular Kodak's Research Systems ENVI software) as a case study for algorithm optimization. The three considered approaches are: 1) Commodity cluster-based parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGA-based hardware implementations. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC models to remote sensing problems

Automated extraction of spectral endmembers is a crucial task in hyperspectral data analysis. In most cases, the computational complexity of endmember extraction algorithms is very high, in particular, for very high-dimensional datasets. However, the intrinsic properties of available techniques are amenable to the design of parallel implementations. In this letter, we evaluate several parallel algorithms that represent three representative approaches to the problem of extracting endmembers. Two parallel algorithms have been selected to represent a first class of algorithms based on convex geometry concepts. In particular, we develop parallel implementations of approximate versions of the N-FINDR and pixel purity index algorithms, along with a parallel hybrid of both techniques. A second class is given by algorithms based on constrained error minimization and represented by a parallel version of the iterative error analysis algorithm. Finally, a parallel version of the automated morphological endmember extraction algorithm is also presented and discussed. This algorithm integrates the spatial and spectral information as opposed to the other discussed algorithms, a feature that introduces additional considerations for its parallelization. The proposed algorithms are quantitatively compared and assessed in terms of both endmember extraction accuracy and parallel efficiency, using standard AVIRIS hyperspectral datasets. Performance data are measured on Thunderhead, a parallel supercomputer at NASA's Goddard Space Flight Center.

As the initial stage of a supervised classification, the quality of training has a significant effect on the entire classification process and its accuracy. In hyperspectral data analysis, a judicious selection of training samples can be tremendously difficult due to the presence of subpixel targets and mixed pixels, in particular, when no prior knowledge about the data is available. For instance, the Multi-Layer Perceptron (MLP) neural network can provide very accurate nonlinear estimations of fractional abundances, provided that the training set contains all possible mixture conditions. However, the requirement of large volumes of training data is a serious limitation in remote sensing because, even if classes concurring to a per-pixel cover class mixture are known, proportions of these classes are very difficult to be estimated a priori. This paper investigates, explores and further proposes solutions to resolve the issues above. Specifically, we develop a nonlinear neural network-based mixture model, coupled with unsupervised algorithms for automated generation of semi-labeled samples that can be effectively used for mixed pixel classification. These unsupervised algorithms, intended for situations where ancilliary information is difficult to be collected prior to data analysis, rely on the principle that patterns that lie close to the location of decision boundaries in feature space are more informative than patterns drawn from the class cores. Computer simulations and real experiments are conducted for performance analysis of nonlinear unmixing techniques based on training samples. KeywordsHyperspectral imaging, Nonlinear mixture analysis, Training samples, Semi-labeled samples, Multi-layer perceptron.

The rapid development in space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral sensors such as the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of efficient techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. Heterogeneous networks of workstations are a very promising cost-effective parallel computing architecture. Unlike traditional homogeneous parallel platforms, heterogeneous architectures are composed of processors running at different speeds. This heterogeneity results in distributed-memory parallel computing systems created from commodity components that can satisfy specific computational requirements for the Earth and space sciences community. This paper explores techniques for mapping hyperspectral image analysis algorithms onto heterogeneous networks of workstations. Important aspects in algorithm design such as portability, reusability and scalability are illustrated by using homogeneous and heterogeneous parallel computing facilities at NASA’s Goddard Space Flight Center and, European Center for Parallelism of Barcelona, and University of Extremadura in Spain. Hyperspectral image data from the AVIRIS data repository is used in experiments, which reveal that heterogeneous networks of workstations are a source of computational power that is both accessible and applicable to obtaining results quickly enough for practical use in information extraction applications from hyperspectral imagery. KeywordsHyperspectral imaging, Parallel algorithms, Heterogeneous computing, Spatial/spectral analysis.

Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the airborne visible infra-red imaging spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wild fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications require a response in real time, few solutions are available to provide fast and efficient analysis of these types of data. This is mainly caused by the dimensionality of hyperspectral images, which limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In the present work, we describe a new parallel methodology which deals with most of the previously addressed problems. The computational performance of the proposed analysis methodology is evaluated using two parallel computer systems, a SGI Origin 2000 shared memory system located at the European Center of Parallelism of Barcelona, and the Thunderhead Beowulf cluster at NASA's Goddard Space Flight Center.

This work describes sequences of extended morphological transformations for filtering and classification of high-dimensional remotely sensed hyperspectral datasets. The proposed approaches are based on the generalization of concepts from mathematical morphology theory to multichannel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Extended morphological transformations, characterized by simultaneously considering the spatial and spectral information contained in hyperspectral datasets, are applied to agricultural and urban classification problems where efficacy in discriminating between subtly different ground covers is required. The methods are tested using real hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer and the German Aerospace Agency Digital Airborne Imaging Spectrometer (DAIS 7915). Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately.

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantially different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied.

Hyperspectral remote sensing increases the detectability of pixel-and subpixel-sized targets by exploiting the finer detail in the spectral signatures. In this paper, we describe a new unsupervised algorithm for the detection of both full pixel and mixed pixel targets in hyperspectral imagery. The proposed method automatically resolves targets by using extended mathematical morphology operations. The performance of the resulting detector is experimentally evaluated using simulated and real hyperspectral data collected by the NASA/Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer and the DLR Reflective Optics System Imaging Spectrometer (ROSIS).

Over the past years, several endmember extraction algorithms have been developed for spectral mixture analysis of hyperspectral data. Due to a lack of quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared using a unified scheme. In this paper, we describe H-COMP, an IDL (Interactive Data Language)-based software toolkit for visualization and interactive analysis of results provided by endmember selection methods. The suitability of using H-COMP for assessment and comparison of endmember extraction algorithms is demonstrated in this work by a comparative analysis of three standard algorithms: Pixel Purity Index (PPI), N-FINDR, and Automated Morphological Endmember Extraction (AMEE). Simulated and real hyperspectral datasets, collected by the NASA/JPL Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), are used to carry out a comparative effort, focused on the definition of reliable endmember quality metrics.

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.