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Hyperspectral images (HSIs) are inevitably contaminated by the mixed noise (such as Gaussian noise, impulse noise, deadlines, and stripes), which could influence the subsequent processing accuracy. Generally, HSI restoration can be transformed into the low-rank matrix recovery (LRMR). In the LRMR, the nuclear norm is widely used to substitute the matrix rank, but its effectiveness is still worth improving. Besides, the l0-norm cannot capture the sparse noise's structured sparsity property. To handle these issues, the adaptive rank and structured sparsity corrections (ARSSC) are presented for HSI restoration. The ARSSC introduces two convex regularizers, that is: 1) the rank correction (RC) and 2) the structured sparsity correction (SSC), to, respectively, approximate the matrix rank and the l2,0-norm. The RC and the SSC can adaptively offset the penalization of large entries from the nuclear norm and the l2,1-norm, respectively, where the larger the entry, the greater its offset. Therefore, the proposed ARSSC achieves a tighter approximation of the noise-free HSI low-rank structure and promotes the structured sparsity of sparse noise. An efficient alternative direction method of multipliers (ADMM) algorithm is applied to solve the resulting convex optimization problem. The superiority of the ARSSC in terms of the mixed noise removal and spatial-spectral structure information preserving, is demonstrated by several experimental results both on simulated and real datasets, compared with other state-of-the-art HSI restoration approaches.

Exploring fast and effective spectral-spatial feature extraction algorithms for hyperspectral image (HSI) classification is one of the most focus problems in current hyperspectral remote-sensing research. Generally, the size of homogeneous regions in HSIs is not consistent in real scenario and real scenario usually consist of ground objects of different scales. Multiscale strategy starts to be used to construct discriminative features at different scales for HSI classification in recent years. To efficiently characterize the multiscale spectral-spatial features of HSIs, a curvature filters-based multiscale feature extraction method with multiscale superpixel segmentation constraint is proposed. The proposed algorithm is composed of the following major stages. First, global multiscale spectral-spatial features are efficiently extracted via progressively curvature filtering and downsampling operations, which can be regarded as an image pyramid decomposition method. Next, a multiscale superpixel segmentation strategy is applied on the first layer of the image pyramid, and a weighted mean operation is applied within and among superpixels to extract the local multiscale spatial features (LMSFs). Finally, the global multiscale curvature features (GMCFs) and the superpixel segmentation-based LMSFs are fused to form the final multiscale spectral-spatial features for classification purposes. To verify the capabilities of the proposed method, comprehensive experiments are performed on five real hyperspectral datasets. Experimental results demonstrate that the proposed method can significantly improve the classification accuracies compared to several standard HSI feature extraction and classification methods, especially when the number of samples for training is limited.

Combining spectral and spatial information can significantly improve the classification performance of hyperspectral image (HSI). Currently, a lot of spectral-spatial HSI classification methods have been proposed. However, the task of HSI classification has remained challenging since the number of training samples is limited in real scenarios. In this article, we propose a novel HSI classification framework with single sample, in which the spectral self-similarity and spatial polygon structure information are fully combined to improve the classification performance. On the one hand, spectral self-similarity is used to expand training samples, which makes it possible to obtain sufficient samples with minimal cost. On the other hand, polygonal partition is introduced to acquire the geometrical structure of land covers in man-made environments. Specifically, the edge information of geometric objects is captured by polygonal partition, which can be utilized to constrain the spatial range of sample expansion and optimize the classification results. Experimental results on three real HSIs illustrate that the proposed method performs very well under small training sample size even when the number of samples is single per class.

Fog in hyperspectral images severely limits the visibility of imaging scene and reduces the image contrast, which has a negative effect on the following image interpretation. Defogging methods aim at restoring a high-quality image from the degraded image. Currently, most dehazing methods mainly depend on the atmospheric scattering model in computer vision and multispectral image communities. However, when these approaches are directly used to remove the fog from HSIs, they cannot produce satisfactory defogging performance. To alleviate this issue, we develop a novel fog model to achieve fog removal from hyperspectral images. First, a fog density map is calculated by differentiating the averaged bands falling into visible and infrared spectral ranges. Then, haze abundance in different spectral bands is estimated based on the pixel reflectance between two selected pixels with different haze levels. Finally, the high-quality hyperspectral image is restored by solving the defogging model. Experiments performed on a new benchmark created by ourselves demonstrate that the proposed method obtains favorable dehazing performance in contrast to other approaches in computer vision and remote sensing fields.

Unmanned aerial vehicle (UAV) hyperspectral imaging has been extensively applied in various fields. However, due to the limited imaging width, hyperspectral images (HSIs) captured by UAV need to be stitched so as to effectively cover the study area. In this paper, an effective seamless stitching method with deep feature matching and elastic warp is proposed for HSIs, which consists of the following major steps. First, for each input HSI, a single band grayscale image is obtained by fusing the bands corresponding to the red, green, and blue wavelengths. Second, the feature points of each HSI are obtained with a robust VGG-style network, and matched with a graph neural network. After point pairs are obtained, the next step is to estimate the transformation matrix of adjacent images, and a spectral correction method based on intrinsic decomposition is proposed to ensure the spectral consistency of adjacent images. In the final stage, a seam-cutting and multi-scale blending strategy is adopted to ensure the spatial consistency of the stitching results. Experimental results on real HSIs show that the proposed method is superior to six representative image stitching approaches.

Hyperspectral image (HSI) and multispectral image (MSI) fusion, which fuses a low-spatial-resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a common scheme to obtain high-resolution HSI (HR-HSI). This article presents a novel HSI and MSI fusion method (called as CNN-Fus), which is based on the subspace representation and convolutional neural network (CNN) denoiser, i.e., a well-trained CNN for gray image denoising. Our method only needs to train the CNN on the more accessible gray images and can be directly used for any HSI and MSI data sets without retraining. First, to exploit the high correlations among the spectral bands, we approximate the desired HR-HSI with the low-dimensional subspace multiplied by the coefficients, which can not only speed up the algorithm but also lead to more accurate recovery. Since the spectral information mainly exists in the LR-HSI, we learn the subspace from it via singular value decomposition. Due to the powerful learning performance and high speed of CNN, we use the well-trained CNN for gray image denoising to regularize the estimation of coefficients. Specifically, we plug the CNN denoiser into the alternating direction method of multipliers (ADMM) algorithm to estimate the coefficients. Experiments demonstrate that our method has superior performance over the state-of-the-art fusion methods.

Glioblastoma (GBM) tumor is the most common primary brain malignant tumor. The precise identification of GBM tumors is very important for diagnosis and treatment. Hyperspectral imaging is a fast, noncontact, accurate, and safe modern medical detection technology, which is expected to be a new tool of intraoperative diagnosis. In order to make full use of the spectral and spatial information of hyperspectral images (HSIs) to achieve accurate GBM tumor identification, a method based on the fusion of multiple deep models is proposed for in vivo human brain HSI classification. The proposed method includes the following major steps: 1) spectral phasor analysis and data oversampling; 2) 1-D deep neural network (1D-DNN)-based spectral HSI feature extraction and classification; 3) 2-D convolution neural network (2D-CNN)-based spectral–spatial HSI feature extraction and classification; 4) edge-preserving filtering-based classification result fusion and optimization; and 5) fully convolutional network (FCN)-based background segmentation. To verify the capabilities of the proposed method, experiments are performed on two real human brain hyperspectral datasets, including 36 in vivo HSIs captured from 16 different patients. The proposed method can achieve an overall accuracy of 96.69% for four-class classification and overall accuracy of 96.34% for GBM tumor identification. Experimental results demonstrate that the proposed method exhibits competitive classification performance and can generate satisfactory thematic maps of the location of the GBM tumor, which can provide the surgeon with guidance on successful and precise tumor resection.

Combining a high-spatial-resolution multispectral image (HR-MSI) with a low-spatial-resolution hyperspectral image (LR-HSI) has become a common way to enhance the spatial resolution of the HSI. The existing state-of-the-art LR-HSI and HR-MSI fusion methods are mostly based on the matrix factorization, where the matrix data representation may be hard to fully make use of the inherent structures of 3-D HSI. We propose a nonlocal sparse tensor factorization approach, called the NLSTF_SMBF, for the semiblind fusion of HSI and MSI. The proposed method decomposes the HSI into smaller full-band patches (FBPs), which, in turn, are factored as dictionaries of the three HSI modes and a sparse core tensor. This decomposition allows to solve the fusion problem as estimating a sparse core tensor and three dictionaries for each FBP. Similar FBPs are clustered together, and they are assumed to share the same dictionaries to make use of the nonlocal self-similarities of the HSI. For each group, we learn the dictionaries from the observed HR-MSI and LR-HSI. The corresponding sparse core tensor of each FBP is computed via tensor sparse coding. Two distinctive features of NLSTF_SMBF are that: 1) it is blind with respect to the point spread function (PSF) of the hyperspectral sensor and 2) it copes with spatially variant PSFs. The experimental results provide the evidence of the advantages of the NLSTF_SMBF method over the existing state-of-the-art methods, namely, in semiblind scenarios.

Most of the existing hyperspectral anomaly detectors are designed based on a single pixel-level feature. These detectors may not adequately utilize spectral–spatial information in hyperspectral images (HSIs) for detecting anomalies. To overcome this problem, this article introduces a novel subpixel-pixel-superpixel guided fusion (SPSGF) method for hyperspectral anomaly detection. This approach comprises three main steps. First, subpixel-, pixel-, and superpixel-level features are extracted from an HSI by employing the spectral unmixing, morphological operation, and superpixel segmentation techniques, respectively. Then, based on the spatial consistency of three features, a guided filtering-based weight optimization technique is developed to construct weight maps for fusion. Finally, a simple yet effective decision fusion method is adopted to utilize the complemental information of three features, and then generates a fused detection result. The performance of the proposed approach is evaluated on three real-scene HSIs and one synthetic HSI. Experimental results validate the advantages of the SPSGF method.

Sun glint in hyperspectral images (HSIs) leads to undesirable spectral interference, which severely affects subsequent image interpretation, such as environmental monitoring of coastal areas. Sun glint removal methods aim to recover a high quality image without sun glint from the original image. Most methods depend on an assumption that the near infrared band is strongly absorbed by water. However, this assumption is not always reliable because the infrared radiation in shallow or turbid water can be reflected back by the seabed or sediment, rather than being fully absorbed. Therefore, the reflected infrared radiation still contains sun glint and these methods cannot sufficiently remove sun glint from HSIs. To address this problem, a texture-aware total variation (TATV)-based method is proposed to remove sun glint from HSIs. The original HSI first is formulated as a desired clean image and a sun glint image. Then, in order to remove the sun glint, we propose a variational model where the different spectral characteristics of sun glint and other surrounding materials are considered. Specifically, we propose a texture-aware total variation regularized method to heavily penalize the variation of the sun glint areas. Experiments performed on simulated and real data sets demonstrate that our method can greatly outperform other state-of-the-art methods in removing sun glint.

Spectral–spatial Gabor filtering, which is based on 3-D local harmonic analysis, has been a powerful spectral–spatial feature extraction tool for hyperspectral image (HSI) classification. However, existing spectral–spatial Gabor approaches are prone to oversmoothing, neglecting the existences of edges and negatively affecting the classification. In this article, we propose a new HSI Gabor filtering concept, called spectral–spatial-range Gabor filtering, which intends to restrain edge interference from disturbing local spectral–spatial harmonic components. Contributions and novelties of our work can be identified as follows: 1) an HSI filtering framework is created, which can accommodate various Gabor filtering procedures and hence offer the potential to guide the design of new Gabor filters; 2) following such a unified filtering framework and taking into consideration both local spectral–spatial harmonic characteristics and range domain variations, we develop a new concept of spectral–spatial-range Gabor filtering; and 3) utilizing this proposed Gabor prototype and elaborating mathematical derivations, we achieve a novel discriminative spectral–spatial-range Gabor filtering method, which can deal with discriminative local harmonics and edge interference simultaneously along the spectral–spatial-range domain, obtaining highly discriminative Gabor features while yielding linear computational complexity. Our novel method is evaluated on four real HSI data sets and achieves excellent performances.

Active learning (AL) attempts to actively select the most representative or useful training samples in an iterative manner. The aim is to simultaneously improve the classification performance and reduce the manual labeling effort. In this article, a novel subpixel-pixel-superpixel-based multiview AL (MAL) (SPS-MAL) method is proposed for hyperspectral image (HSI) classification. Here, the multiple views are generated via extracting the subpixel-level, pixel-level, and superpixel-level information. The multiple views can reflect various characteristics of HSI, i.e., spectral mixture, spectral discrimination, and spectral–spatial structure. Therefore, the joint use of diverse and complementary information in multiple views will contribute to a better identification ability of different classes. In addition, a coarse-to-fine MAL algorithm is introduced to effectively select the most representative samples with the most uncertainty. Specifically, a disagreement analysis on multiple views and joint posterior probability estimation is used to query unlabeled samples. Along with the expansion of training samples, view-specific confidence scores are estimated to adaptively integrate the classification results of multiple views, according to their discrimination performance. In this way, the classification accuracy will be further boosted while the number of necessary training samples can be significantly reduced. The experimental classification results on three well-known HSIs demonstrate the effectiveness of the proposed SPS-MAL method.

In this paper, an edge-preserving filtering and principal component analysis (PCA)-based visualization method is proposed for hyperspectral images, in which both global and local image information of hyperspectral images (HSIs) are taken into account in the proposed visualization framework that consists of the following major steps. First, the band number of the original image is reduced with averaging-based image fusion (AIF). Then, the edge-preserving filtering is performed on the dimension reduced image so as to decompose it into two components, i.e., the base layers which contain the large-scale boundary information, and the detail layers which contain the mid- and small-scale edges and textures. Next, the base layers are fused with the principal component analysis method, and the detail layers are fused with the weighted sum method, in which the fusion weights are determined by the transform matrix of the base layers. Finally, the fused detail layers are enhanced with histogram equalization and combined with the fused base layers to visualize the hyperspectral image. Experimental results on four real hyperspectral data sets demonstrate that the proposed approach performs the best in improving image contrast and preserving the details. In addition, compared with seven state-of-the-art visualization methods, the quantitative metrics obtained by the proposed method on four data sets have been increased by 80.45%, 69.29%, 138.61%, and 23.21% on average in terms of separability of features (SF), standard deviation (SD), average gradient (AG), and entropy (EN).

Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes.

Hyperspectral unmixing (HU) is an active research topic in the remote-sensing community. It aims at modeling mixed pixels using a collection of pure constituent materials ( endmembers ) weighted by their corresponding fractional abundances. Among existing unmixing schemes, nonnegative matrix factorization (NMF) has drawn significant attention due to its unsupervised nature, as well as its capacity to obtain both endmembers and fractional abundances simultaneously. In this article, we present a new blind unmixing method based on the generalized morphological component analysis (GMCA) framework, in which an additional constraint is introduced into the standard NMF model to represent the sparsity and morphological diversity of the abundance maps associated with each endmember. More specifically, we take into account the fact that different ground categories in a hyperspectral scene generally exhibit various spatial distributions and morphological characteristics. As a result, when providing a specific dictionary basis for these categories, their corresponding abundance maps (referred to as sources ) can be sparsely represented. In addition, due to the low correlation between different sources, their sparse representations will not share the same most significant coefficients. With this observation in mind, we can further promote source discrimination and separation in the unmixing process. Moreover, in order to obtain a stable solution of the involved optimization problem, we adopt an alternate iterative constrained algorithm with a threshold descent strategy. Our experiments, carried out on both synthetic and real hyperspectral scenes, reveal that our newly developed GMCA-based unmixing method obtains very promising results with fast convergence speed and requiring significantly less parameter tuning. This confirms the advantage of the proposed spatial morphological component approach for HU purposes.

Hyperspectral unmixing (HU) is an efficient way to extract component information from mixed pixels in remotely sensed imagery. Nonnegative matrix factorization (NMF) based unmixing methods have been widely used due to their ability to extract endmembers (pure spectral signatures) and their corresponding fractional abundances in simultaneous fashion. In this article, we propose a new sparse-constrained NMF method for HU purposes. Unlike most sparse regularizers, imposed on abundances (vectors or matrix) directly, our method imposes sparse constraints on a transformed abundance domain. It is based on the assumption that sparsity, when applied on a well-designed transform domain, leads to sparser representations than those in the corresponding source domain (e.g., natural images are approximately sparse in a wavelet domain). In this regard, we specifically explore sparsity on a curvelet transformed domain of abundances. Moreover, we consider the Chambolle-Pock algorithm to solve the involved optimization model, so as to obtain a fast and stable solution. Our experiments, carried out on both synthetic and real hyperspectral datasets, reveal that our newly proposed transform domain-based sparse NMF unmixing method (hereinafter named as “TS-NMF”) obtains better unmixing results than those achieved by other state-of-the-art NMF-based unmixing methods, with less parameter tuning and better robustness to noise.

In this article, a novel hyperspectral anomaly detection method with kernel Isolation Forest (iForest) is proposed. The method is based on an assumption that anomalies rather than background can be more susceptible to isolation in the kernel space. Based on this idea, the proposed method detects anomalies as follows. First, the hyperspectral data are mapped into the kernel space, and the first $K$ principal components are used. Then, the isolation samples in the image are detected with the iForest constructed using randomly selected samples in the principal components. Finally, the initial anomaly detection map is iteratively refined with locally constructed iForest in connected regions with large areas. Experimental results on several real hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art methods.

Traditional hyperspectral imaging technique obtains numerous hyperspectral images (HSIs) with hundreds of spectral bands, leading to high cost in data acquisition, transmission, and storage. Compressed sensing (CS) theory provides a new imaging mechanism, which relies on the assumption that signals can be sparsely represented over a dictionary. By the CS imaging technique, original HSIs can be approximately reconstructed from only a few sampled measurements. In this article, a novel context-aware CS (CACS) method for HSIs is proposed by incorporating contextual prior to the dictionary learning and the sparse reconstruction. First, a patch-based online dictionary learning (ODL) algorithm is developed by introducing a joint sparse constraint. On the one hand, the online dictionary learning mechanism enables a more adaptive representation of HSIs with different scenes than using fixed-basis-based dictionaries, e.g., the discrete cosine transform (DCT) and the discrete wavelet transform dictionaries. On the other hand, the introduced joint sparse constraint promotes the learned dictionary to more sparsely and structurally represent spectral pixels. Then, with the well-learned dictionary, a weighted smoothing regularization is introduced to develop a new sparse reconstruction model. Considering the high spectral–spatial similarity of pixels in a neighborhood, the new sparse reconstruction model will encourage a locally smoothing reconstruction result. In this way, the spectral–spatial structures of the HSI can be well preserved, while possible artifacts can be effectively reduced. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art hyperspectral compressive imaging methods.

In this article, a novel hyperspectral image (HSI) classification method based on fusing multiple edge-preserving operations (EPOs) is proposed, which consists of the following steps. First, the edge-preserving features are obtained by performing different types of EPOs, i.e., local edge-preserving filtering and global edge-preserving smoothing on the dimension-reduced HSI. Then, with the assistance of a superpixel segmentation method, the edge-preserving features are further improved by considering the inter and intra spectral properties of superpixels. Finally, the spectral and edge-preserving features are fused to form one composite kernel, which is fed into the support vector machine (SVM) followed by a majority voting fusion scheme. Experimental results on three data sets demonstrate the superiority of the proposed method over several state-of-the-art classification approaches, especially when the training sample size is limited. Furthermore, 21 well-known methods, including mathematical morphology-based approaches, sparse representation models, and deep learning-based classifiers, are adopted to be compared with the proposed method on Houston data set with standard sets of training and test samples released during 2013 Data Fusion Contest, which also shows the effectiveness of the proposed method.

In this paper, a novel local Brownian descriptor (LBD) based feature extraction method is proposed for hyperspectral image (HSI) classification. Compared with the classical correlation feature that only characterizes the linear relationship among spectral bands, the LBD can measure both linear and nonlinear relationships to provide much richer spatial-spectral information for HSI classification. Specifically, the HSI is firstly mapped into a low-dimensional spectral subspace by utilizing the maximum noise fraction (MNF) method. Based on the subspace, pixels in the square neighborhood of each sample construct a sample block. Then, the LBD as the local correlation feature is calculated for each sample block. Finally, a kernel sparse representation (KSR) classifier is utilized on these feature descriptors, leading to the final classification results. Experiments conducted on a real hyperspectral image demonstrate the outstanding performance of the proposed method over several state-of-the-art methods.

In this paper, a novel unmanned aerial vehicle (UAV) hyperspectral image stitching framework based on radiation correction and seam-cutting blending is proposed. Firstly, spectral correlation constraints are introduced to eliminate mismatched pairs in the transform matrix estimation step. Then, a spectral correction method based on intrinsic images is proposed to ensure spectral consistency of stitching results. In order to obtain more natural stitching results without edge effect, a seam-cutting and multi-scale blending strategy is adopted in the final blending stage. Experimental results on real unmanned aerial vehicle hyperspectral strip images show that the proposed method is superior to a representative image stitching approach.

It is well known that classification accuracy highly relies on the number of labeled samples. However, it is difficult to obtain sufficient labeled samples in real-world applications. To solve this issue, a novel hyperspectral image (HSI) classification method based on polygonal partition is proposed for crop mapping. This method only needs single sample per class as an initial training set. Specifically, multiscale polygonal partition is applied on the first three components of the HSI. Then, a spectral similarity-based sample expansion method is proposed to obtain more labeled samples. Next, a pixel-wise classifier, the support vector machine (SVM), is used to acquire an initial classification result. Finally, classification result is further optimized according to the partition maps. Experimental results show that classification performance of the proposed method is satisfactory even when the number of labeled sample is single for each class.

Fusion of hyperspectral images (HSIs) and multispectral images (MSIs) with different resolutions is an active research topic in the field of remote sensing. However, HSI-MSI fusion assessments in existing researches are basically conducted on simulated data gone through spectral or spatial downsampling, which cannot reflect the actual performances of fusion methods in application scenarios. To conquer this problem, a new remote sensing dataset- Hunan UAV-borne HSIs and MSIs fusion(HNU-HMiF) dataset is provided in this paper. The proposed dataset contains fine registered hyperspectral and multispectral image pairs captured by unmanned aerial vehicle (UAV) covering different ground objects, and can be used to evaluate, select, and even develop fusion methods for users or researchers. Successful applications including method evaluation and comparison confirm the validity and reliability of the proposed dataset.

Hyperspectral image classification has been a constant hot topic in remote sensing field, and achieved significant progress recently. Until now, most of the existing works are based on high-quality noise-free datasets, whereas in real applications, the images are often degraded by different types of noise, which makes the noise sensitivity become one of the key issues for classification assessment. In this paper, we study the noise effects on hyperspectral image classification including Guassian, salt-and-pepper, and stripe noise. The experimental results shows that noise has varying degrees of negative effects on different hyperspectral classification methods, which provides instructional information for method design and selection under noise environment in actual classification applications.

Most of the existing hyperspectral anomaly detectors only consider the difference between anomaly pixels and background pixels. These methods may mistakenly detect some regions in a complex background that contains various ground covers, since some background regions and anomaly objects may have similar spectral or spatial characteristics. Therefore, with a new perspective, this paper introduces a novel manifold ranking-based detection framework (MRDF). In addition to capturing the difference between anomaly pixels and background pixels, this detection framework exploits the similarity between anomaly pixels for detection. Specifically, the proposed detection framework comprises three main steps. First, the Reed–Xiaoli method is applied to capture the spectral difference between anomaly pixels and background pixels, and an initial detection map can be obtained. A set of anomaly queries are obtained automatically by employing the binary segmentation to the initial detection map. Then, we construct a closed-loop graph to characterize the spatial similarity between adjoining nodes where each node is a superpixel. Finally, a manifold ranking technique is employed to estimate the ranking value of every node based on the similarity between the test node and anomaly queries. By normalizing the ranking value of each node, a final detection map is generated. Abundant experiments are conducted on four real hyperspectral data sets. It is found that the proposed detection framework obtains a better detection performance than the current state-of-the-art detectors.

Fusing a high spatial resolution multispectral image (HR-MSI) with a low spatial resolution hyperspectral image (LR-HSI) of the same scenario to acquire a high spatial resolution hyperspectral image (HR-HSI) has recently attracted more and more attention. We propose a novel spatial-spectral sparse representation (SSSR) based approach for the fusion of an HR-MSI and an LR-HSI of the same scenario in this paper. In the proposed SSSR method, we formulate the fusion problem as the estimation of spectral basis and coefficients from the LR-HSI and HR-MSI. To better model the spatial and spectral characteristics of the HR-HSI, we incorporate the non-local spatial similarities, priors of the spectral unmixing, and a sparse prior to the fusion problem. Meanwhile, instead of keeping the spectral basis fixed, we design the alternative optimization algorithm for the estimation of spectral basis and coefficients, which can achieve the accurate reconstruction. Experimental results on both non-blind fusion and blind fusion cases demonstrate the effectiveness of the SSSR approach.

Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results.

In this paper, a novel multi-scale total variation method is proposed to extract structural features from hyperspectral images (HSIs), which consists of the following steps. First, the spectral dimension of the HSI is reduced with an averaging-based method. Then, the multi-scale structural features (MSFs), which are insensitive to image noise, are constructed with a relative total variation-based structure extraction technique. Finally, the MSFs are fused together using kernel principal component analysis (KPCA), so as to obtain the KPCA-fused MSFs for classification. Experimental results on three publicly available hyperspectral datasets, including both well-known, long-used data, and a recent dataset obtained from an international contest, demonstrate the competitive performance over several state-of-the-art classification approaches in this field. Moreover, the robustness of the proposed method to the small-sample-size problem and serious image noise is also demonstrated.

Mislabeled training samples may have a negative effect on the performance of hyperspectral image classification. In order to solve this problem, a new density peak (DP) clustering-based noisy label detection method is proposed, which consists of the following steps. First, the distances among the training samples of each class are calculated using four representative distance metrics, i.e., the Euclidean distance (ED), orthogonal projection divergence (OPD), spectral information divergence (SID), and correlation coefficient (CC). Then, the local density of each training sample can be obtained using the DP clustering algorithm. Finally, a local density-based decision function is used to detect the noisy labels. The effectiveness of the proposed method is evaluated using the support vector machines on several real hyperspectral data sets. Experimental results demonstrate that the proposed noisy label detection method indeed helps in improving the classification performance.

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.

In the field of hyperspectral image classification, a widely used way for objective performance evaluation of different classification methods is calculating three accuracy indexes, i.e., the overall accuracy, the average accuracy, and the Kappa coefficient. These accuracy indexes are obtained by comparing the classification results with the ground truth, i.e., a reference classification map labeled by human experts. In this paper, the effect of ground truths on the objective performance evaluation of hyperspectral image classification is studied. The purpose is to investigate, if the ground truth is insufficient, whether the above accuracy indexes can be completely responsible. Furthermore, in order to measure the robustness of different classification methods to those insufficient ground truths, four evaluation metrics, i.e., the Pearson linear correlation coefficient, root-mean-square error, Spearman’s rank correlation coefficient, and Kendall’s rank correlation coefficient have been adopted for further analysis. Based on these experiments, an interesting conclusion can be obtained that insufficient ground truths may limit the assessment capability of existing accuracy indexes. This underlines that overoptimistic performance evaluations may exist and stresses the demand of designing more appropriate accuracy indexes for objective performance evaluation with insufficient ground truths.

In this letter, a novel edge-preserving filtering based approach is proposed for feature extraction of hyperspectral images, which consists of the following steps. First, the dimension of t...

Hyperspectral image (HSI) sharpening, which aims at fusing an observable low spatial resolution (LR) HSI (LR-HSI) with a high spatial resolution (HR) multispectral image (HR-MSI) of the same scene to acquire an HR-HSI, has recently attracted much attention. Most of the recent HSI sharpening approaches are based on image priors modeling, which are usually sensitive to the parameters selection and time-consuming. This paper presents a deep HSI sharpening method (named DHSIS) for the fusion of an LR-HSI with an HR-MSI, which directly learns the image priors via deep convolutional neural network-based residual learning. The DHSIS method incorporates the learned deep priors into the LR-HSI and HR-MSI fusion framework. Specifically, we first initialize the HR-HSI from the fusion framework via solving a Sylvester equation. Then, we map the initialized HR-HSI to the reference HR-HSI via deep residual learning to learn the image priors. Finally, the learned image priors are returned to the fusion framework to reconstruct the final HR-HSI. Experimental results demonstrate the superiority of the DHSIS approach over existing state-of-the-art HSI sharpening approaches in terms of reconstruction accuracy and running time.

Hyperspectral imaging plays an important role for mineral mapping in a nondestructive and noninvasive way. In this paper, a novel resolution enhancement method is proposed based on the principle of intrinsic image decomposition for mineral mapping. This method is based on an assumption that hyperspectral image (HSI) can be decomposed into a reflectance component and an illumination component. Based on this idea, the RGB image is first transformed into Intensity-Hue-Saturation (IHS) space, and the intensity channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled intensity channel. Finally, the HSI with high resolution can be reconstructed by utilizing the estimated illumination and the reflectance components. Experimental results validate the effectiveness of the proposed method qualitatively and quantitatively by outperforming several state-of-the-art approaches.

In this paper, in order to extract efficient spectral-spatial features for hyperspectral image classification, a Gaussian curvature (GC) filter based feature extraction method with multiscale segmentation constraint is proposed. The method consists of the following major steps: First, the maximum noise fraction (MNF) method is applied on the hyperspectral images (HSIs) to reduce the noise and computational complexity. The GC features are extracted from the dimension reduced HSIs via the GC filter. Next, a multiscale segmentation strategy is applied on the HSIs, and the multiscale spatial features are extracted by applying the weighted mean operations within and among superpixels. Finally, the GC features and dimension reduced multiscale spatial features are fused to form the final multiscale Gaussian curvature features (MGCFs) for classification purposes. To verify the effectiveness of the proposed method, we conduct experiments on the Indian Pines data set. Experimental results demonstrate that the proposed method can significantly improve the classification accuracies compared to several standard classification methods.

Sun glint, as the spectral reflection of solar radiation on non-flat water surfaces, is a serious confounding factor for coastal shallow-water environments. When the coastal areas are observed with a hyperspectral sensor, the existing sun glint in the produced images can seriously influence the quality of the image interpretation. To solve this issue, in this paper, we propose a novel sun glint removal method based on a variation model for hyperspectral images (HSIs). The proposed method aims to decompose the original HSI into a desired clean image and a sun glint image. To achieve this, we exploit a texture-aware total variation to remove the sun glint in HSIs, where the texture information is imposed on the total variation regularization to highlight sun glint. Experiments on simulated and real datasets demonstrate that our method can obtain outstanding performance with respect to other state-of-the-art approaches.

Noise usually appears in hyperspectral images (HSIs), and strongly affects the performance of the follow processing and analysis. In recent years, a large number of denoising algorithms have been proposed and it is known that the denoising effect is highly dependent on the accurate estimates of the type and level of noise present in an HSI. This paper focuses on analyzing the real noise in HSIs by separating and estimating the level of noise in HSIs. In consideration of the spectral correlation and the unique spatial structure of stripe noise, the developed method employs Fourier domain analysis and the high correlation among neighboring spectral bands to separate different types of noise. Experimental results show that the level of noise may be quite different for different bands of an HSI, and HSIs captured by different senors or in different scenes.

Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial resolution owing to the limitations of imaging sensors. Image fusion is an effective and economical way to enhance the spatial resolution of HSI, which combines HSI with higher spatial resolution multispectral image (MSI) of the same scenario. In the past years, many HSI and MSI fusion algorithms are introduced to obtain high-resolution HSI. However, it lacks a full-scale review for the newly proposed HSI and MSI fusion approaches. To tackle this problem, this work gives a comprehensive review and new guidelines for HSI–MSI fusion. According to the characteristics of HSI–MSI fusion methods, they are categorized as four categories, including pan-sharpening based approaches, matrix factorization based approaches, tensor representation based approaches, and deep convolution neural network based approaches. We make a detailed introduction, discussions, and comparison for the fusion methods in each category. Additionally, the existing challenges and possible future directions for the HSI–MSI fusion are presented.

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance. The task of spectral-spatial hyperspectral image classification has remained challenging because of high intraclass spectrum variability and low interclass spectral variability. This fact has made the extraction of spatial information highly active. In this work, a novel hyperspectral image classification framework using the fusion of dual spatial information is proposed, in which the dual spatial information is built by both exploiting pre-processing feature extraction and post-processing spatial optimization. In the feature extraction stage, an adaptive texture smoothing method is proposed to construct the structural profile (SP), which makes it possible to precisely extract discriminative features from hyperspectral images. The SP extraction method is used here for the first time in the remote sensing community. Then, the extracted SP is fed into a spectral classifier. In the spatial optimization stage, a pixel-level classifier is used to obtain the class probability followed by an extended random walker-based spatial optimization technique. Finally, a decision fusion rule is utilized to fuse the class probabilities obtained by the two different stages. Experiments performed on three data sets from different scenes illustrate that the proposed method can outperform other state-of-the-art classification techniques. In addition, the proposed feature extraction method, i.e., SP, can effectively improve the discrimination between different land covers., Comment: 13 pages, 11 figures

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.

In this letter, a novel anomaly detection method is proposed, which can effectively fuse the multiscale information extracted by attribute and edge-preserving filters. The proposed method consists of the following steps. First, multiscale attribute and edge-preserving filters are utilized to obtain multiscale anomaly detection maps. Then, the multiscale detection maps are fused via an averaging approach, and the training samples of the anomalies and background are selected from the fused detection map. Next, the support vector machine classification is performed on the hyperspectral image to obtain an anomaly probability map. Finally, the detection result is obtained by multiplying the fused detection map and the anomaly probability map, followed by an edge-preserving filtering-based postprocessing. Experiments performed on four real hyperspectral data sets demonstrate that the proposed method shows a better detection performance with respect to several state-of-the-art hyperspectral anomaly detection methods.

Airborne and spaceborne hyperspectral imaging systems have advanced in recent years in terms of spectral and spatial resolution, which makes data sets produced by them a valuable source for land-cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image classification techniques by taking advantage of both spectral and spatial information in a single classification framework. The ECHO (Extraction and Classification of Homogeneous Objects) classifier, which was proposed in 1976, might be the first spectral-spatial classification approach of its kind in the remote sensing community. Since then and especially in the latest years, increasing attention has been dedicated to developing sophisticated spectral-spatial classification methods. There is now a rich literature on this particular topic in the remote sensing community, composing of several fast-growing branches. In this paper, the latest advances in spectral-spatial classification of hyperspectral data are critically reviewed. More than 25 approaches based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning are addressed with an emphasis on discussing their methodological foundations. Examples of experimental results on three benchmark hyperspectral data sets, including both well-known long-used data and a recent data set resulting from an international contest, are also presented. Moreover, the utilized training and test sets for the aforementioned data sets as well as several codes and libraries are also shared online with the community.

In this paper, we propose a segmented principal component analysis (SPCA) and Gaussian pyramid decomposition based multiscale feature fusion method for the classification of hyperspectral images. First, considering the band-to-band cross correlations of objects, the SPCA method is utilized for the spectral dimension reduction of the hyperspectral image. Then, the dimension-reduced image is decomposed into several Gaussian pyramids to extract the multiscale features. Next, the SPCA method is performed again to compute the fused SPCA based Gaussian pyramid features (SPCA-GPs). Finally, the performance of the SPCA-GPs is evaluated using the support vector machine classifier. Experiments performed on three widely used hyperspectral images show that the proposed SPCA-GPs method outperforms several compared classification methods in terms of classification accuracies and computational cost.

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large amount of training samples in order to avoid overfitting. Additionally, it is a typical non-convex problem affected by many local minima and flat regions. To address these problems, in this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space, and hence improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and thus yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set., This paper has been accepted by IEEE TNNLS

Image decolorization is known to be an effective way in transferring a color image into a gray one while well preserving the major information of all three bands. In this paper, a simple yet effective hyperspectral image visualization framework based on decolorization, named decolorization based hyperspectral visualization, is proposed, which enables us to fully exploit the benefits of decolorization technique. The proposed framework consists of the following two main steps. First, the hyperspectral image is partitioned into nine subsets of adjacent hyperspectral bands and the averaged band of each subset is calculated. Then, the dimension reduced image is further divided into three groups of adjacent bands, and the bands in each group are fused by using an image decolorization method. The main contribution of this paper is that the strong correlations in two different fields, i.e., image decolorization and hyperspectral image visualization, are first built. Experiments performed on several real hyperspectral data sets demonstrate that the proposed framework can obtain outstanding visualization performance in terms of both subjective and objective evaluations.

Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI) has attracted increasing interest in recent years. In this paper, we propose a coupled sparse tensor factorization (CSTF) based approach for fusing such images. In the proposed CSTF method, we consider an HR-HSI as a three-dimensional tensor and redefine the fusion problem as the estimation of a core tensor and dictionaries of the three modes. The high spatial-spectral correlations in the HR-HSI are modeled by incorporating a regularizer which promotes sparse core tensors. The estimation of the dictionaries and the core tensor are formulated as a coupled tensor factorization of the LR-HSI and of the HR-MSI. Experiments on two remotely sensed HSIs demonstrate the superiority of the proposed CSTF algorithm over current state-of-the-art HSI-MSI fusion approaches.

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved good performance. In general, deep models adopt a large number of hierarchical layers to extract features. However, excessively increasing network depth will result in some negative effects (e.g., overfitting, gradient vanishing, and accuracy degrading) for conventional convolutional neural networks. In addition, the previous networks used in HSI classification do not consider the strong complementary yet correlated information among different hierarchical layers. To address the above two issues, a deep feature fusion network (DFFN) is proposed for HSI classification. On the one hand, the residual learning is introduced to optimize several convolutional layers as the identity mapping, which can ease the training of deep network and benefit from increasing depth. As a result, we can build a very deep network to extract more discriminative features of HSIs. On the other hand, the proposed DFFN model fuses the outputs of different hierarchical layers, which can further improve the classification accuracy. Experimental results on three real HSIs demonstrate that the proposed method outperforms other competitive classifiers.

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.

In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor filtering on the first three principal components of the hyperspectral image, which can typically characterize the low-level spatial structures of different orientations and scales. Then, the Gabor features and spectral features are simply stacked to form the fused features. Afterwards, deep features are captured by training a stacked sparse autoencoder deep network with the fused features obtained above as inputs. Since the number of training samples of hyperspectral images is often very limited, which negatively affects the classification performance in deep learning, an effective way of constructing virtual samples is designed to generate more training samples, automatically. By jointly utilizing both the real and virtual samples, the parameters of the deep network can be better trained and updated, which can result in classification results of higher accuracies. Experiments performed on four real hyperspectral datasets show that the proposed method outperforms several recently proposed classification methods in terms of classification accuracies.

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the last four decades from being a sparse research tool into a commodity product available to a broad user community. Specially, in the last 10 years, a large number of new techniques able to take into account the special properties of hyperspectral data have been introduced for hyperspectral data processing, where hyperspectral image classification, as one of the most active topics, has drawn massive attentions. Spectral–spatial hyperspectral image classification can achieve better classification performance than its pixel-wise counterpart, since the former utilizes not only the information of spectral signature but also that from spatial domain. In this paper, we provide a comprehensive overview on the methods belonging to the category of spectral–spatial classification in a relatively unified context. First, we develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral–spatial methods. Based on such, the categorizations of single-dependency, bilayer-dependency, and multiple-dependency systems are further introduced. Second, we categorize the performings of existing spectral–spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e., preprocessing-based, integrated, postprocessing-based, and hybrid classifications. Then, typical methodologies are outlined. Finally, several representative spectral–spatial classification methods are applied on real-world hyperspectral data in our experiments.