Information Of Hyperspectral Imagery Research Articles

Attention-Enhanced Generative Adversarial Network for Hyperspectral Imagery Spatial Super-Resolution

Hyperspectral imagery (HSI) with high spectral resolution contributes to better material discrimination, while the spatial resolution limited by the sensor technique prevents it from accurately distinguishing and analyzing targets. Though generative adversarial network-based HSI super-resolution methods have achieved remarkable progress, the problems of treating vital and unessential features equally in feature expression and training instability still exist. To address these issues, an attention-enhanced generative adversarial network (AEGAN) for HSI spatial super-resolution is proposed, which elaborately designs the enhanced spatial attention module (ESAM) and refined spectral attention module (RSAM) in the attention-enhanced generator. Specifically, the devised ESAM equipped with residual spatial attention blocks (RSABs) facilitates the generator that is more focused on the spatial parts of HSI that are difficult to produce and recover, and RSAM with spectral attention refines spectral interdependencies and guarantees the spectral consistency at the respective pixel positions. Additionally, an especial U-Net discriminator with spectral normalization is enclosed to pay more attention to the detailed informations of HSI and yield to stabilize the training. For producing more realistic and detailed super-resolved HSIs, an attention-enhanced generative loss is constructed to train and constrain the AEGAN model and investigate the high correlation of spatial context and spectral information in HSI. Moreover, to better simulate the complicated and authentic degradation, pseudo-real data are also generated with a high-order degradation model to train the overall network. Experiments on three benchmark HSI datasets illustrate the superior performance of the proposed AEGAN method in HSI spatial super-resolution over the compared methods.

Bayesian Subpixel Mapping of Hyperspectral Imagery via Discrete Endmember Variability Mixture Model and Markov Random Field

Although Bayesian methods have been very effective for spatial-spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced sub-pixel mapping (SPM) by simultaneously considering several key issues i.e., endmember variability, the discrete nature of subpixel class labels and the spatial information in HSI. Therefore, we propose a new Bayesian SPM method based on discrete endmember variability mixture model (DEMM) and Markov random field (MRF), which has three main characteristics. First, DEMM allows the advanced SPM by completely accounting for the endmember-abundance patterns of each pixel to accommodate the endmember variability, the discrete hidden class label field of subpixels while taking into account the noise heterogeneity effect. Second, the discrete class label field modeled by MRF together with the DEMM, which can be integrated into a novel Bayesian model to better exploit the spatial contextual and spectral information. Third, the resulting Bayesian model can be efficiently solved by a designed expectation-maximization (EM) iteration, where E-step estimates the subpixel class label field using a simulated annealing (SA) algorithm and M-step estimates the endmembers for each pixel in HSI using alternating nonnegative least squares (ANLS) approach. The experimental results on three HSI datasets demonstrate that the proposed approach outperforms previously available SPM techniques.

A Multiscale Superpixel-Level Group Clustering Framework for Hyperspectral Band Selection

Hyperspectral imagery (HSI) contains hundreds of bands, which provide a wealth of spectral information and enable better characterization of features. However, the excessive dimensionality also poses a dimensional disaster for subsequent processing. Fortunately, band selection (BS) gives a straightforward and effective way to pick out a subset of bands with rich information and low correlation. Although many hyperspectral BS methods, especially clustering-based ones, have been proposed by researchers in recent years, the contextual information of adjacent bands and the spatial structural information of materials are not well investigated. Therefore, in this article, a multiscale superpixel-level group-clustering framework (MSGCF) has been proposed for hyperspectral BS. Different from previous, a new superpixel-level distance measure is elaborately utilized to group and cluster the spectral bands, which jointly considers the spectral context and spatial structure information. Concretely, to preserve the spatial structural information of HSI, multiple superpixel segmentation is first performed to generate superpixel maps in multiscales, which enables complementarity of multiple superpixel segmentation algorithms and adaptation to diverse scales of land cover types. Second, the grouping and clustering paradigm is introduced to conduct the contextual information among bands. Here the maximum points of superpixel-level KL- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> distance of adjacent bands are adopted as partition points to separate bands into groups, which encourages adjacent bands with strong correlation to be divided into the same group. Third, a superpixel-level fast density-based clustering method (SuFDPC) with superpixel-level <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2, 1}$ </tex-math></inline-formula> distance is developed to select representative bands in every group. Finally, BS results are achieved with a ranking-based voting strategy by concerning information entropy and frequency of occurrence in a unified scheme. A series of ablation analyses and experimental comparisons on four real HSI datasets have been conducted, as well as similarity comparisons for the selected bands. The experimental results consistently demonstrated the effectiveness of our MSGCF approach. The codes of this work will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://jiasen.tech/papers/</uri> for the sake of reproducibility.

Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing

Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and the effects of spectral variability in hyperspectral imaging, the ability to accurately and effectively unmixing the complex HSI still remains limited. To this end, this article presents a novel blind HU model, called sparsity-enhanced convolutional decomposition (SeCoDe), by jointly capturing spatial–spectral information of HSI in a tensor-based fashion. SeCoDe benefits from two perspectives. On the one hand, the convolutional operation is employed in SeCoDe to locally model the spatial relation between the targeted pixel and its neighbors, which can be well explained by spectral bundles that are capable of addressing spectral variabilities effectively. It maintains, on the other hand, physically continuous spectral components by decomposing the HSI along with the spectral domain. With sparsity-enhanced regularization, an alternative optimization strategy with alternating direction method of multipliers (ADMM)-based optimization algorithm is devised for efficient model inference. Extensive experiments conducted on three different data sets demonstrate the superiority of the proposed SeCoDe compared to previous state-of-the-art methods. We will also release the code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/danfenghong/IEEE_TGRS_SeCoDe</uri> to encourage the reproduction of the given results.

Hyperspectral Unmixing Using Nonlocal Similarity-Regularized Low-Rank Tensor Factorization

Recently, methods based on nonnegative tensor factorization (NTF), which benefits from the tensor representation of hyperspectral imagery (HSI) without any information loss, have attracted increasing attention. However, most existing methods fail to explore the internal spatial structure of data, resulting in low unmixing performance. Moreover, when the algorithm is optimized, the solution is unstable. In this article, a regularizer based on nonlocal tensor similarity is proposed, which can not only fully preserve the global information of HSI but also mine the internal information of data in the spatial domain. HSI is regarded as a 3-D tensor and is directly subjected to endmember extraction and abundance estimation. To fully explore the structural characteristics of data, we simultaneously use the local smoothing and low tensor rank prior of the data to constrain the unmixing model. First, several 4-D tensor groups can be obtained after the nonlocal similarity structure of HSI is learned. Subsequently, a low tensor rank prior is applied to each 4-D tensor, which can fully simulate the nonlocal similarity in the image. In addition, total variation (TV) is also used to explore the local spatial relationship of data, which can generate a smooth abundance map through edge preservation. The optimization is solved by the ADMM algorithm. Experiments on synthetic and real data illustrate the superiority of the proposed method.

P.141 Topological data analysis for genetic-driven stratification of patients with major depressive disorder

The clustering of hyperspectral images is a challenging task because of the high dimensionality of the data. Sparse subspace clustering (SSC) algorithm is one of the popularly used clustering algorithm for high dimensionality data. However, SSC has not fully used the spectral and spatial information during similarity matrix construction based on single sparse representation coefficient for hyperspectral Imagery (HSI) clustering. In this paper, two novel similarity matrix construction methods named as Cosine-Euclidean similarity matrix (abbreviated as CE) and Cosine-Euclidean dynamic weighting similarity matrix (abbreviated as CEDW) are proposed for HSI clustering. They can combine the high spectral information and rich spatial information. Firstly, CE utilizes the cosine similarity of spectral information based on overall sparse representation vectors and classical Euclidean distance of spatial information to construct a novel similarity matrix. Secondly, inheriting CE merits, dynamic weighting adjustment method is introduced to CEDW for some external influence factors to the HSI information. Several experiments on HSI demonstrated that the proposed algorithms are effective for HSI clustering.

Multiple Characteristics Similarity Metric Method for Hyperspectral Image Classification

The traditional classification algorithms have been widely applied for hyperspectral imagery (HSI), but many methods exploit spectral or spatial information in HSI data that doesn't make full use of the information of HSI data. To solve this problem, a new classification method, termed multiple characteristics similarity metric (MCSM), was proposed in this paper for HSI classification. In MCSM, a spatial similarity probability relationship, a sparse similarity relationship and a collaborative similarity relationship are integrated to use the multiple information of HSI. At first, a spatial similarity probability is designed by the multiple features fusion, which can reveal the spatial information of HSI. Then, it utilizes sparse representation and collaborative representation to represent the sparse and collaborative properties of HSI. Finally, the class label can be determined by combining spatial similarity probability, sparse similarity and collaborative similarity. To demonstrate the effectiveness of the proposed method, experiments have been conducted on the Indian Pines and Pavia University data sets. The experimental results show that MCSM achieves better classification performance compared with some state-of-the-art classification methods, which indicates that MCSM can make full use of the multiple information of HSI to form the complementarity of different characteristics and enhance the discriminant performance for HSI classification.

Hyperspectral Target Detection Based on Tensor Sparse Representation

The sparse representation-based detection (SRD) algorithm has already shown the effectiveness for hyperspectral target detection (TD) recently. However, SRD does not utilize spatial information of hyperspectral imagery (HSI). In this letter, a novel tensor SRD (TSRD) algorithm is proposed to take jointly the spatial and the spectral information of HSI into account. TSRD extends each atom of both the target and the background dictionaries into a third-order tensor where the local spatial neighborhood information can be well preserved. It not only possesses the advantages of SRD that no assumptions about the target and background distributions are required and spectral variability can be considered but also exploits the spatial information of HSI to further increase the accuracy of TD. The experimental results on both synthetic and real hyperspectral data show that the proposed TSRD method outperforms traditional and state-of-the-art TD methods in terms of detection accuracy.

Hyperspectral anomaly detection using spectral–spatial features based on the human visual system

ABSTRACT Anomaly detection (AD) is one of the most attracting topics within the recent 10 years in hyperspectral imagery (HSI). The goal of the AD is to label the pixels with significant spectral or spatial differences to their neighbours, as targets. In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the retina and the visual cortex functionality, the multiscale multiresolution analysis is applied to some principal components of hyperspectral data, to extract features from different spatial levels of the image. Then the global and local relations between features are considered based on inspiring the visual attention mechanism and inferotemporal (IT) part of the visual cortex. The effects of the attention mechanism are implemented using the logarithmic function which well highlights, small variations in pixels’ grey levels in global features. Also, the maximum operation is used over the local features for imitating the function of IT. Finally, the information theory concept is used for generating the final detection map by weighting the global and local detection maps to obtain the final anomaly map. The result of the proposed method is compared with some state-of-the-art methods such as SSRAD, FLD, PCA, RX, KPCA, and AED for two well-known real hyperspectral data which are San Diego airport and Pavia city, and a synthetic hyperspectral data. The results demonstrate that the proposed method effectively improves the AD capabilities, such as enhancement of the detection rate, reducing the false alarm rate and the computation complexity.

Unsupervised Bayesian Classification of a Hyperspectral Image Based on the Spectral Mixture Model and Markov Random Field

Typical unsupervised classification of hyperspectral imagery (HSI) uses a Gaussian mixture model to determine intensity similarity of pixels. However, the existence of mixed pixels in HSI tends to reduce the effectiveness of the similarity measure and leads to large classification errors. Since a semantic class is always dominated by a particular endmember, a mixed pixel can be better classified by identifying the dominant endmember. By exploiting the spectral mixture model (SMM) that describes the endmember-abundance pattern of mixed pixels, the discriminative ability of HSI can be enhanced. A Bayesian classification approach is presented for spatial–spectral HSI classification, where the data likelihood is built upon the SMM, and the label prior is based on a Markov random field (MRF). The new approach has three key characteristics. First, instead of using intensity similarity, the new approach uses the abundance-endmember pattern of each pixel and classifies a pixel by its dominant endmember. Second, to integrate the SMM into a Bayesian framework, a data likelihood is designed based on the SMM to reflect the influence of the dominant endmember on the conditional distribution of the mixed pixel given the class label. Third, the resulting maximum a posteriori problem is solved by the expectation–maximization (EM) algorithm, in which the E-step adopts a graph-cut approach to estimate the class labels, and the M-step adopts a purified-means approach to estimate the endmembers. Experiments on both simulated and real HSIs demonstrate that the proposed method can exploit the spatial–spectral information of HSI to achieve high accuracy in unsupervised classification of HSI.

A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery

The clustering of hyperspectral images is a challenging task because of the high dimensionality of the data. Sparse subspace clustering (SSC) algorithm is one of the popularly used clustering algorithm for high dimensionality data. However, SSC has not fully used the spectral and spatial information during similarity matrix construction based on single sparse representation coefficient for hyperspectral Imagery (HSI) clustering. In this paper, two novel similarity matrix construction methods named as Cosine-Euclidean similarity matrix (abbreviated as CE) and Cosine-Euclidean dynamic weighting similarity matrix (abbreviated as CEDW) are proposed for HSI clustering. They can combine the high spectral information and rich spatial information. Firstly, CE utilizes the cosine similarity of spectral information based on overall sparse representation vectors and classical Euclidean distance of spatial information to construct a novel similarity matrix. Secondly, inheriting CE merits, dynamic weighting adjustment method is introduced to CEDW for some external influence factors to the HSI information. Several experiments on HSI demonstrated that the proposed algorithms are effective for HSI clustering.

A spectral-spatial method based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection

ABSTRACTRecently, some methods based on low-rank and sparse matrix decomposition (LRASMD) have been developed to improve the performance of hyperspectral anomaly detection (AD). However, these methods mainly take advantage of the spectral information in hyperspectral imagery (HSI), and ignore the spatial information. This article proposes an LRASMD-based spectral-spatial (LS-SS) method for hyperspectral AD. First, the Go Decomposition (GoDec) algorithm is employed to solve the low-rank background component and the sparse anomaly component. Next, the sparse component is explored to calculate the spectral sparsity divergence index (SDI). Based on spectral SDI, the detection result in the spectral domain and the reliable background points, which are employed as training data to construct the background manifold by linear local tangent space alignment (LLTSA), can also be obtained. Then, based on the background manifold and the transformation matrix, the low-dimensional manifold of the whole data is computed by linear mapping. After that, the kernel collaborative representation detector (KCRD) is used in the low-dimensional manifold of the whole data for the spatial SDI. Finally, SS SDI is computed for the final detection result. The theoretical analysis and experimental results demonstrate that the proposed LS-SS can achieve better performance when compared with the comparison algorithms.

Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary

The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI.

Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

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.

A Subspace-Based Change Detection Method for Hyperspectral Images

Remote sensing change detection has played an important role in many applications. Most traditional change detection methods deal with single-band or multispectral remote sensing images. Hyperspectral remote sensing images offer more detailed information on spectral changes so as to present promising change detection performance. The challenge is how to take advantage of the spectral information at such a high dimension. In this paper, we propose a subspace-based change detection (SCD) method for hyperspectral images. Instead of dealing with band-wise changes, the proposed method measures spectral changes. SCD regards the observed pixel in the image of Time 2 as target and constructs the background subspace using the corresponding pixel in the image of Time 1, and additional information. In this paper, two types of additional information, i.e., spatial information in the neighborhood of the corresponding pixel in Time 1, and the spectral information of undesired land-cover types, are used to construct the background subspace for special applications. The subspace distance is calculated to determine whether the target is anomalous with respect to the background subspace. The anomalous pixels are considered as changes. Here, orthogonal subspace projection is employed to calculate the subspace distance, which makes full use of the advantage of the abundant spectral information in hyperspectral imagery, and is also easy to apply. The experimental results using Hyperion data and HJ-1A HSI data indicate that SCD gives more accurate detection results, with a lower false alarm rate, compared with other state-of-the-art methods. SCD with additional information also gives satisfactory results in the experiments, reducing the false alarms caused by misregistration and suppressing the change of undesired land-cover types.