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In this paper, a new joint spectral–spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spectral–spatial unified network (SSUN) with an end-to-end architecture for the hyperspectral image (HSI) classification. Different from traditional spectral–spatial classification frameworks where the spectral feature extraction (FE), spatial FE, and classifier training are separated, these processes are integrated into a unified network in our model. In this way, both FE and classifier training will share a uniform objective function and all the parameters in the network can be optimized at the same time. In the implementation of the SSUN, we propose a band grouping-based long short-term memory model and a multiscale convolutional neural network as the spectral and spatial feature extractors, respectively. In the experiments, three benchmark HSIs are utilized to evaluate the performance of the proposed method. The experimental results demonstrate that the SSUN can yield a competitive performance compared with existing methods. [ABSTRACT FROM AUTHOR]

In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of the geometrical properties of hyperspectral signatures. Experimental results on both real and synthetic data sets show that the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mixtures present in the pixels of the scene. [ABSTRACT FROM PUBLISHER]

In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature. [ABSTRACT FROM PUBLISHER]

Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently, a spectral–spatial adaptive hyperspectral total variation (SSAHTV) was proposed and obtained promising results. However, the SSAHTV model is insensitive to the image details, which makes the edges blur. To overcome all of these drawbacks, a spectral–spatial kernel method for HSI denoising is proposed in this paper. The proposed method is inspired by the observation that the spectral–spatial information is highly redundant in HSIs, which is sufficient to estimate the clear images. In this paper, a spectral–spatial kernel regularization is proposed to maintain the spectral correlations in spectral dimension and to match the original structure between two spatial dimensions. Moreover, an adaptive mechanism is developed to balance the fidelity term according to different noise distributions in each band. Therefore, it cannot only suppress noise in the high-noise band but also preserve information in the low-noise band. The reliability of the proposed method in removing noise is experimentally proved on both simulated data and real data. [ABSTRACT FROM PUBLISHER]

High-spectral resolution imaging systems play a critical role in the identification and characterization of objects in a scene of interest. Unfortunately, multiple factors impair spectral resolution, as in the case of modern snapshot spectral imagers that associate each hyperpixel with a specific spectral band. In this paper, we introduce a novel postacquisition computational technique aiming to enhance the spectral dimensionality of imaging systems by exploiting the mathematical frameworks of sparse representations and dictionary learning. We propose a coupled dictionary learning model which considers joint feature spaces, composed of low- and high-spectral resolution hypercubes, in order to achieve spectral superresolution performance. We formulate our spectral coupled dictionary learning optimization problem within the context of the alternating direction method of multipliers, and we manage to update the involved quantities via closed-form expressions. In addition, we consider a realistic spectral subsampling scenario, taking into account the spectral response functions of different satellites. Moreover, we apply our spectral superresolution algorithm on real satellite data acquired by Landsat-8 and Sentinel-2 sensors. Finally, we have investigated the problem of hyperspectral image unmixing using the recovered high-spectral resolution data cube, and we are able to demonstrate that the proposed scheme provides significant value in hyperspectral image understanding techniques. Experimental results demonstrate the ability of the proposed approach to synthesize high-spectral-resolution 3-D hypercubes, achieving better performance compared to state-of-the-art resolution enhancement methods. [ABSTRACT FROM AUTHOR]

Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method. [ABSTRACT FROM AUTHOR]

High-spatial hyperspectral (HH) image reconstruction using both high-spatial multispectral (HM) image and low-spatial hyperspectral (LH) image over the same scene is widely used in many real applications. Nevertheless, the pair of HM image and LH image over the same scene is hard to obtain. To solve this problem, a new HH image reconstruction method using spectral library-based dictionary learning (named as HIRSL) is proposed in this paper, only from one HM image. The above reconstruction problem is formulated in the framework of sparse representation, as an estimation of the band matching matrix, the spectral dictionary, and the sparse coefficients. More specifically, a band matching method is proposed for mapping the common spectral library to a specific spectral library corresponding to the reconstructed HH image in spectral domain. Then, an efficient spectral dictionary learning method is proposed for the construction of spectral dictionary using the matched specific spectral library, which avoids the dependence of the LH image over the same scene. Finally, the sparse coefficients of the HM image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers without nonnegative constraint. Comparison results on simulated and real data sets with the relative state-of-the-art methods demonstrate that even only using one HM image, our proposed method achieves a comparable reconstruction quality of high-spatial hyperspectral image both in spatial and spectral domains. [ABSTRACT FROM AUTHOR]

In blind hyperspectral unmixing (HU), the pure-pixel assumption is well known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are heavily mixed. In the no-pure-pixel case, a good blind HU approach to consider is the minimum volume enclosing simplex (MVES). Empirical experience has suggested that MVES algorithms can perform well without pure pixels, although it was not totally clear why this is true from a theoretical viewpoint. This paper aims to address the latter issue. We develop an analysis framework wherein the perfect endmember identifiability of MVES is studied under the noiseless case. We prove that MVES is indeed robust against lack of pure pixels, as long as the pixels do not get too heavily mixed and too asymmetrically spread. The theoretical results are supported by numerical simulation results. [ABSTRACT FROM PUBLISHER]

Traditional spectral unmixing methods are usually based on the linear mixture model (LMM) or nonlinear mixture model (NLMM), in which only the additive noise is considered. However, in hyperspectral applications, the additive, multiplicative, and mixed noises play important roles. In this paper, we propose an antinoise model for hyperspectral unmixing. In the antinoise model, all the additive, multiplicative and mixed noises are addressed. To deal with the problems faced by LMM or NLMM and to tackle the antinoise model, an antinoise model based hyperspectral unmixing method is presented, where block coordinate descent is employed to solve an approximated $L_0$ norm constraint, then a nonnegative matrix factorization (NMF) method is presented, which is based on the bounded Itakura–Saito divergence. The experimental results on both synthetic and real hyperspectral data sets demonstrate the efficacy of the proposed model and the corresponding method. [ABSTRACT FROM PUBLISHER]

Classification is one of the most important techniques to the analysis of hyperspectral remote sensing images. Nonetheless, there are many challenging problems arising in this task. Two common issues are the curse of dimensionality and the spatial information modeling. In this paper, we present a new general framework to train series of effective classifiers with spatial information for classifying hyperspectral data. The proposed framework is based on the two key observations: 1) the curse of dimensionality and the high feature-to-instance ratio can be alleviated by using random subspace (RS) ensembles; and 2) the spatial–contextual information is modeled by the extended multiattribute profiles (EMAPs). Two fast learning algorithms, i.e., decision tree (DT) and extreme learning machine (ELM), are selected as the base classifiers. Six RS ensemble methods, namely, RS with DT, random forest (RF), rotation forest, rotation RF (RoRF), RS with ELM (RSELM), and rotation subspace with ELM (RoELM), are constructed by the multiple base learners. Experimental results on both simulated and real hyperspectral data verify the effectiveness of the RS ensemble methods for the classification of both spectral and spatial information (EMAPs). On the University of Pavia Reflective Optics Spectrographic Imaging System image, our proposed approaches, i.e., both RSELM and RoELM with EMAPs, achieve the state-of-the-art performances, which demonstrates the advantage of the proposed methods. The key parameters in RS ensembles and the computational complexity are also investigated in this paper. [ABSTRACT FROM PUBLISHER]

Deep neural networks have been shown to be useful for the classification of hyperspectral images, particularly when a large amount of labeled data is available. However, we may not have enough reference data to train a deep neural network for many practical geospatial image analysis applications. To address this issue, in this paper, we propose to use a deep feature alignment neural network to carry out the domain adaptation, where the labeled data from a supplementary data source can be utilized to improve the classification performance in a domain where otherwise limited labeled data are available. In the proposed model, discriminative features for the source and target domains are first extracted using deep convolutional recurrent neural networks and then aligned with each other layer-by-layer by mapping features from each layer to transformed common subspaces at each layer. Experimental results are presented with two data sets. One of these data sets represents domain adaptation between images acquired at different times, while the other data set represents a very unique and challenging domain adaptation problem, representing source and target images that are acquired using different hyperspectral imagers that collect data from different viewpoints and platforms (a ground-based forward-looking street view of objects acquired at the close range and an aerial hyperspectral image). We demonstrate that the proposed deep learning framework enables the robust classification of the target domain data by leveraging information from the source domain. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to detect and correct mislabeled training samples for hyperspectral image classification. First, domain transform recursive filtering-based feature extraction is used to improve the separability of the training samples. Then, constrained energy minimization-based object detection is performed on the training set with each training sample serving as the object spectrum. Finally, the label of each training sample is verified or corrected based on the averaged detection probabilities of different classes. Experiments performed on real hyperspectral data sets demonstrate the effectiveness of the proposed method in improving classification performance with respect to the classifier trained with the original training set that contains a number of mislabeled samples. [ABSTRACT FROM AUTHOR]

In the community of remote sensing, nonlinear mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel nonlinear spectral unmixing method following the recent multilinear mixing model of Heylen and Scheunders, which includes an infinite number of terms related to interactions between different endmembers. The proposed unmixing method is unsupervised in the sense that the endmembers are estimated jointly with the abundances and other parameters of interest, i.e., the transition probability of undergoing further interactions. Nonnegativity and sum-to-one constraints are imposed on abundances while only nonnegativity is considered for endmembers. The resulting unmixing problem is formulated as a constrained nonlinear optimization problem, which is solved by a block coordinate descent strategy, consisting of updating the endmembers, abundances, and transition probability iteratively. The proposed method is evaluated and compared with existing linear and nonlinear unmixing methods for both synthetic and real hyperspectral data sets acquired by the airborne visible/infrared imaging spectrometer sensor. The advantage of using nonlinear unmixing as opposed to linear unmixing is clearly shown in these examples. [ABSTRACT FROM PUBLISHER]

Feature extraction is of significance for hyperspectral image (HSI) classification. Compared with conventional hand-crafted feature extraction, deep learning can automatically learn features with discriminative information. However, two issues exist in applying deep learning to HSIs. One issue is how to jointly extract spectral features and spatial features, and the other one is how to train the deep model when training samples are scarce. In this paper, a deep convolutional neural network with two-branch architecture is proposed to extract the joint spectral–spatial features from HSIs. The two branches of the proposed network are devoted to features from the spectral domain as well as the spatial domain. The learned spectral features and spatial features are then concatenated and fed to fully connected layers to extract the joint spectral–spatial features for classification. When the training samples are limited, we investigate the transfer learning to improve the performance. Low and mid-layers of the network are pretrained and transferred from other data sources; only top layers are trained with limited training samples extracted from the target scene. Experiments on Airborne Visible/Infrared Imaging Spectrometer and Reflective Optics System Imaging Spectrometer data demonstrate that the learned deep joint spectral–spatial features are discriminative, and competitive classification results can be achieved when compared with state-of-the-art methods. The experiments also reveal that the transferred features boost the classification performance. [ABSTRACT FROM PUBLISHER]

Supervised classification of hyperspectral images (HSI) is a very challenging task due to the existence of noisy and mixed spectral characteristics. Recently, the widely developed spectral unmixing techniques offer the possibility to extract spectral mixture information at a subpixel level, which can contribute to the categorization of seriously mixed spectral pixels. Besides, it has been demonstrated that the discrimination between different materials will be improved by integrating the geometry and structure information, which can be derived from the variance between neighboring pixels. Furthermore, by incorporating the spatial context, the superpixel-based spectral–spatial similarity information can be used to smooth classification results in homogeneous regions. Therefore, a novel fusion framework for HSI classification that combines subpixel, pixel, and superpixel-based complementary information is proposed in this paper. Here, both feature fusion and decision fusion schemes are introduced. For the feature fusion scheme, the first step is to extract subpixel-level, pixel-level, and superpixel-level features from HSI, respectively. Then, the multiple feature-induced kernels are fused to form one composite kernel, which is incorporated with a support vector machine (SVM) classifier for label assignment. For the decision fusion scheme, class probabilities based on three different features are estimated by the probabilistic SVM classifier first. Then, the class probabilities are adaptively fused to form a probabilistic decision rule for classification. Experimental results tested on different real HSI images can demonstrate the effectiveness of the proposed fusion schemes in improving discrimination capability, when compared with the classification results relied on each individual feature. [ABSTRACT FROM PUBLISHER]

Convolutional neural network (CNN) is well known for its capability of feature learning and has made revolutionary achievements in many applications, such as scene recognition and target detection. In this paper, its capability of feature learning in hyperspectral images is explored by constructing a five-layer CNN for classification (C-CNN). The proposed C-CNN is constructed by including recent advances in deep learning area, such as batch normalization, dropout, and parametric rectified linear unit (PReLU) activation function. In addition, both spatial context and spectral information are elegantly integrated into the C-CNN such that spatial-spectral features are learned for hyperspectral images. A companion feature-learning CNN (FL-CNN) is constructed by extracting fully connected feature layers in this C-CNN. Both supervised and unsupervised modes are designed for the proposed FL-CNN to learn sensor-specific spatial-spectral features. Extensive experimental results on four benchmark data sets from two well-known hyperspectral sensors, namely airborne visible/infrared imaging spectrometer (AVIRIS) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed C-CNN outperforms the state-of-the-art CNN-based classification methods, and its corresponding FL-CNN is very effective to extract sensor-specific spatial-spectral features for hyperspectral applications under both supervised and unsupervised modes. [ABSTRACT FROM PUBLISHER]

Hyperspectral imagery has emerged as a popular sensing modality for a variety of applications, and sparsity-based methods were shown to be very effective to deal with challenges coming from high dimensionality in most hyperspectral classification problems. In this paper, we challenge the conventional approach to hyperspectral classification that typically builds sparsity-based classifiers directly on spectral reflectance features or features derived directly from the data. We assert that hyperspectral image (HSI) processing can benefit very significantly by decoupling data into geometrically distinct components since the resulting decoupled components are much more suitable for sparse representation-based classifiers. Specifically, we apply morphological separation to decouple data into texture and cartoon-like components, which are sparsely represented using local discrete cosine bases and multiscale shearlets, respectively. In addition to providing a structured sparse representation, this approach allows us to build classifiers with invariance properties specific to each geometrically distinct component of the data. The experimental results using real-world HSI data sets demonstrate the efficacy of the proposed framework for classifying multichannel imagery under a variety of adverse conditions—in particular, small training sample size, additive noise, and rotational variabilities between training and test samples. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel superpixel-based intrinsic image decomposition (SIID) framework for hyperspectral images. Intrinsic images are usually referred to the separation of shading and reflectance components from an input image. Considering the high dimensionality of hyperspectral images, we further decompose the shading component into the product of environment illumination and surface orientation changes, thus modeling the problem more properly. The proposed method consists of the following steps. First, we build two superpixel segmentation maps of different scales, i.e., a finer one that is oversegmented and a coarser one that is undersegmented. Based on the observation that the finer superpixel map achieves a higher segmentation accuracy, whereas the coarser superpixel map tends to reserve the objectness of the original image, we model the SIID decomposition problem in a matrix form based on the finer superpixel map and define a constraint matrix by integrating the information in the coarser superpixel map. The constraint matrix is introduced as a secondary constraint in order to make the ill-posed IID problem solvable. Finally, we transform the original decomposition problem into minimizing the Frobenius norm of the proposed matrix energy function and iteratively derive the solution. Our experimental results demonstrate that the proposed method is able to achieve a performance outperforming the state-of-the-art while making a great improvement in efficiency. [ABSTRACT FROM PUBLISHER]

Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands. Although many efforts have been made to this topic, the structural information has not yet been well exploited during band selection, and there are still several deficiencies in search strategies, leaving room for further improvement. This paper tackles the unsupervised hyperspectral band selection problem from a global perspective and proposes a novel method claiming the following main contributions: 1) structure-aware measures for band informativeness and independence; and 2) a graph formulation of band selection allowing for an efficient integrated search by means of dominant set extraction. Experiments on three real hyperspectral images demonstrate the superiority of the proposed band selector in comparison with benchmark methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spectral-spatial feature extraction and classification framework based on an artificial neuron network in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervision symbol that combines softmax loss and center loss is adopted to train the proposed network, by which intraclass features are gathered while interclass variations are enlarged. Based on the learned architecture, the extracted spectrum-based features are classified by a center classifier. Moreover, to fuse the spectral and spatial information, an adaptive spectral-spatial center classifier is developed, where multiscale neighborhoods are considered simultaneously, and the final label is determined using an adaptive voting strategy. Finally, experimental results on three well-known data sets validate the effectiveness of the proposed methods compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers. [ABSTRACT FROM PUBLISHER]

Spectral unmixing is a popular technique for analyzing remotely sensed hyperspectral data sets with subpixel precision. Over the last few years, many algorithms have been developed for each of the main processing steps involved in spectral unmixing (SU) under the LMM assumption: 1) estimation of the number of endmembers; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the abundance of endmembers in the scene. Although this general processing chain has proven to be effective for unmixing certain types of hyperspectral images, it also has some drawbacks. The first one comes from the fact that the output of each stage is the input of the following one, which favors the propagation of errors within the unmixing chain. A second problem is the huge variability of the results obtained when estimating the number of endmembers of a hyperspectral scene with different state-of-the-art algorithms, which influences the rest of the process. A third issue is the computational complexity of the whole process. To address the aforementioned issues, this paper develops a novel negative abundance-oriented SU algorithm that covers, for the first time in the literature, the main steps involved in traditional hyperspectral unmixing chains. The proposed algorithm can also be easily adapted to a scenario in which the number of endmembers is known in advance and two additional variations of the algorithm are provided to deal with high-noise scenarios and to significantly reduce its execution time, respectively. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the presented method is highly competitive (in terms of both unmixing accuracy and computational performance) with regard to other SU techniques with similar requirements, while providing a fully self-contained unmixing chain without the need for any input parameters. [ABSTRACT FROM PUBLISHER]

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives. [ABSTRACT FROM PUBLISHER]

This paper presents a variational-based approach for fusing hyperspectral and multispectral images. The fusion problem is formulated as an inverse problem whose solution is the target image assumed to live in a lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the supports of the corresponding active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with state-of-the-art fusion methods. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an optimization-based method for simultaneous fusion and unsupervised segmentation of hyperspectral remote sensing images by exploiting redundancy in the data. The hyperspectral data set is visualized as a single image obtained by weighted addition of all spectral points at each pixel location in the data set. The weights are optimized to improve those statistical characteristics of the fused image, which invoke an enhanced response from a human observer. A piecewise-constant smoothness constraint is imposed on the weights instead of the fused image by minimization of its 3-D total-variation norm, thus preventing the fused image from blurring. The optimal recovery of the weight matrix additionally provides useful information in segmenting the hyperspectral data set spatially. We provide ample experimental results to substantiate the usefulness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, a new sparse representation-based binary hypothesis (SRBBH) model for hyperspectral target detection is proposed. The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. The sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionary under the alternative hypothesis. The sparse vectors in the model can be recovered by a greedy algorithm, and the same sparsity levels are employed for both hypotheses. Thus, the recovery process leads to a competition between the background-only subspace and the target and background subspace, which are directly represented by the different hypotheses. The detection decision can be made by comparing the reconstruction residuals under the different hypotheses. Extensive experiments were carried out on hyperspectral images, which reveal that the SRBBH model shows an outstanding detection performance. [ABSTRACT FROM PUBLISHER]

Hyperspectral remote sensing often captures imagery where the spectral profiles of the spatial pixels are the result of the reflectance contribution of numerous materials. Spectral unmixing is then used to extract the collection of materials, or endmembers, contained in the measured spectra and a set of corresponding fractions that indicate the abundance of each material present at each pixel. This paper aims at developing a spectral unmixing algorithm directly from compressive measurements acquired using the coded-aperture snapshot spectral imaging (CASSI) system. The proposed method first uses the compressive measurements to find a sparse vector representation of each pixel in a 3-D dictionary formed by a 2-D wavelet basis and a known spectral library of endmembers. The sparse vector representation is estimated by solving a sparsity-constrained optimization problem using an algorithm based on the variable splitting augmented Lagrangian multipliers method. The performance of the proposed spectral unmixing method is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The optimal coded apertures are designed such that the CASSI sensing matrix satisfies a restricted isometry property with high probability. Simulations with synthetic and real hyperspectral cubes illustrate the accuracy of the proposed unmixing method. [ABSTRACT FROM PUBLISHER]

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

A new hyperspectral unmixing algorithm is proposed based on the normal compositional model (NCM) to estimate the endmembers and abundance parameters jointly in this paper. The NCM considers the hyperspectral imaging as a stochastic process and interprets each pixel value as a random vector, which is linearly mixed by the endmembers. More precisely, these endmembers are also treated as random variables as opposed to deterministic values in order to capture spectral variability that is not well described by the linear mixing model (LMM). However, the higher complexity of such an unmixing model leads to more difficulty in parameter estimation. A particle swarm optimization-expectation maximization (PSO-EM) algorithm, a “winner-take-all” version of the EM, is proposed to solve the parameter estimation problem, which employs a partial E step. The main contribution of the proposed PSO-EM is making optimum use of particle swarm optimization method (PSO) in the partial E step, which solves the difficulty of the integrals in the NCM model. The performance of the proposed methodology is evaluated through synthetic and real data experiments. Our obtained results demonstrate the superior performance of PSO-EM compared to other NCM-based as well as LMM-based methods. [ABSTRACT FROM PUBLISHER]

Band selection, by choosing a set of representative bands in a hyperspectral image, is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) an optimal clustering framework, which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint; 2) a rank on clusters strategy, which provides an effective criterion to select bands on existing clustering structure; and 3) an automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared with some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperforms the other methods on various data sets. [ABSTRACT FROM AUTHOR]

It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial, and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this paper, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection process, conducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well. [ABSTRACT FROM PUBLISHER]

Anomaly detection is playing an increasingly important role in hyperspectral image (HSI) processing. The traditional anomaly detection methods mainly extract knowledge from the background and use the difference between the anomalies and the background to distinguish them. Anomaly contamination and the inverse covariance matrix problem are the main difficulties with these methods. The low-rank and sparse matrix decomposition (LRaSMD) technique may have the potential to solve the aforementioned hyperspectral anomaly detection problem since it can extract knowledge from both the background and the anomalies. This paper proposes an LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection (LSMAD). This approach has the following capabilities: 1) takes full advantage of the LRaSMD technique to set the background apart from the anomalies; 2) explores the low-rank prior knowledge of the background to compute the background statistics; and 3) applies the Mahalanobis distance differences to detect the probable anomalies. Extensive experiments were carried out on four HSIs, and it was found that LSMAD shows a better detection performance than the current state-of-the-art hyperspectral anomaly detection methods. [ABSTRACT FROM PUBLISHER]

Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers. [ABSTRACT FROM PUBLISHER]

In this paper, a subtle relationship is found between the volume of a subsimplex and the volume gradient of a simplex with respect to hyperspectral images. By using this relationship, we propose an efficient band selection method, namely, the volume-gradient-based band selection (VGBS) method. The VGBS method is an unsupervised method, which tries to remove the most redundant band successively. Interestingly, the VGBS method can find the most redundant band based only on the gradient of volume instead of calculating the volumes of all subsimplexes. Experiments on simulated and real hyperspectral data verify the efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

In recent years, dimensionality reduction (DR) and classification have become important issues of hyperspectral image analysis. In this paper, we propose a new spatial-spectral similarity measure, which maps the distances between two image patches in hyperspectral images. Including spatial information by using the spatial neighbors, the proposed similarity measure is based on the fact that the observed pixels in the images are spatially related, and the meaningful features can be extracted from both the spectral and spatial domains. First, the new similarity measure can effectively exploit the rich spectral and spatial structures of data, thus improving the original k-nearest neighbor (kNN) classification methods. Second, the new similarity measure can be incorporated into existing DR methods including linear or nonlinear techniques. With the merits of the proposed similarity measure, the modified DR methods become effective in dealing with the redundancy resulting from spectral signature as well as the spatial relation among pixels. A comparative study and analysis based on classification experiments using five real hyperspectral data sets, which were acquired by different instruments, is conducted to evaluate the proposed similarity measure. The experimental results demonstrate that the proposed measure is promising for combining spectral and spatial information when applied to DR and classification of hyperspectral data sets. [ABSTRACT FROM PUBLISHER]

A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches. [ABSTRACT FROM AUTHOR]

A novel band discrimination analysis framework for hyperspectral image (HSI) supervised classification is proposed based on dual density (DD). Different from the popular supervised band selection (BS) approaches which measure the discrimination among classes under multivariate normal distribution hypothesis, our work infers the class discrimination degree (overlapping extent) for valid extraction of band subset without any assumed distribution. In the proposed framework, it is crucial to find indexes to measure the discrimination degree of each band, and therefore we develop the DD indexes, including the homogeneity density and the heterogeneity density. Viewing each band of the HSI as a data set, i.e., the data points in each data set are 1-D, and we first obtain the DD value pairs for all data points in each data set. Then, for each data set, we determine its discrimination degree using DD-based zone ratio or score quantify strategy. Finally, the bands, which are determined as the nonoverlapped or have high scores, are chosen as the band subset for the subsequent classification. Superiorities of the proposed BS are demonstrated on the three real-world HSIs over several well-known BS algorithms in terms of classification accuracy and speed. [ABSTRACT FROM AUTHOR]

We present a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object's spectral signature. Our method utilizes class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques in spectral correlation domain to perform the object detection task. The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results, in terms of receiver operating characteristic (ROC) curves and area-under-ROC (AUROC), on three popular real-world hyperspectral data sets demonstrate the superiority of the proposed CSFJTC technique over other well-known hyperspectral object detection approaches. [ABSTRACT FROM PUBLISHER]

A sparsity-based model has led to interesting results in hyperspectral image (HSI) classification. Sparse representation from a test sample is used to identify the class label. However, an ℓ1-based sparse algorithm sometimes yields unstable sparse representation. Inspired by recent progress in manifold learning, two manifold-based sparse representation algorithms are proposed to exploit the local structure of the test samples in corresponding sparse representations for enforcing smoothness across neighboring samples' sparse representations. Using techniques from regularization and local invariance, two manifold-based regularization terms are incorporated into the ℓ1-based objective function. Extensive experiments show that our proposed algorithms obtain excellent classification performance on three classic HSIs. [ABSTRACT FROM PUBLISHER]

Low-rank representation (LRR) can construct the relationships among pixels for hyperspectral image (HSI) classification with a given dictionary and a noise term. However, the accuracy of HSI classification based on LRR methods is degraded with the redundant and noise information existed in pixels. The neglect of semantic information around pixels in the LRR methods may cause “salt-and-pepper” problem in HSI classification. To avoid the aforementioned problems, a novel self-supervised low-rank representation method called SSLRR is developed. In SSLRR, the LRR and spectral–spatial graph regularization are developed as the pixel-level constraints to remove the redundant and noise information in HSIs. Superpixel constraints including data structure and relationship construction are further utilized to provide supervised feedback information to the subspace learning to avoid the “salt-and-pepper” problem generated in the pixel-based classification methods, and simultaneously enhance the performance of LRR. The pixel-level and superpixel-level regularizations are explicitly integrated into a unified objective function for LRR. By means of the linearized alternating direction method with adaptive penalty, the solution to the objective function is achieved by employing a customized iterative algorithm. We perform comprehensive evaluation of the proposed method on three challenging public HSI data sets. We obtain new state-of-the-art performance on these data sets, and achieve improvements of 44.3%, 13.4%, and 30.1% in overall accuracy compared to the best LRR method. [ABSTRACT FROM AUTHOR]