In this letter, based on tensor modeling, we propose a novel panchromatic (Pan)-guided tensor low-rank (PGTLR) model for multispectral image (MSI) sharpening, which aims to fuse the low resolution (LR) MSI and Pan image to output the high resolution (HR) MSI. On one hand, we novelly exploit the tensor low-fibered-rank prior of HR MSI to model its global three-dimensional spatial-spectral correlations, which is constructed as the tensor nuclear norm (TNN) prior term. On the other hand, we further novelly exploit the Pan-guided tensor low-fibered-rank prior to model the spatial link between HR MSI and Pan, which is constructed as the novel Pan-guided TNN prior term. Furthermore, the proposed PGTLR model is optimized by an efficient alternative algorithm. Moreover, the experimental results on reduced-scale and full-scale datasets quantitatively and visually validate the superiority of PGTLR.

In this article, we propose a novel, unified, and effective hyperspectral image (HSI) destriping and denoising method with gradient subspace-regularized HSI and stripe-coupled nonconvex tensor low-rank priors (GSHSNTLRs). First, by exploiting the mode-3 low-rank properties of the gradients of HSI (i.e., the spatial horizontal gradient, spatial vertical gradient, and spectral gradient of HSI) along the spectral dimension, we apply the mode-3 low-rank decomposition of the gradients of HSI to obtain their representation coefficient tensors (RCTs), and further study the tensor low-tubal-rank properties of the RCTs in the gradient subspace. Thus, we propose the unified gradient subspace-regularized log tensor nuclear norm (LogTNN)-based nonconvex tensor low-rank prior term of the RCTs. Moreover, by fully considering the structural speciality of stripe noise, which has strong tensor low-tubal-rank property, we particularly study the HSI-guided tensor low-rank modeling for the stripe noise by exploring the tensor low-tubal-rank property of HSI plus stripe and propose the unified HSI and stripe-coupled LogTNN-based nonconvex tensor low-rank prior term of HSI and stripe simultaneously. Subsequently, the proposed GSHSNTLR model is solved by using the alternating direction method of multipliers (ADMMs). Finally, lots of experimental results and analysis fully demonstrate the destriping and denoising performance and superiority of GSHSNTLR.

Supervised classification of hyperspectral image (HSI) is generally required to obtain better performance in spectral-spatial feature learning by fully using complex pixel- and superpixel-level interdependencies with small labeled samples. Limited by the local regular convolutions, convolutional neural networks (CNNs) can only exploit information from the short-range Euclidean neighbors of a target, hindering the effectiveness of feature representation. In contrast, graph convolutional networks (GCNs) can learn long-range dependencies between non-Euclidean neighbors but usually require the input of a full graph constructed from a whole HSI, making GCNs must be trained in a full-batch manner with tremendous computational consumption. In this work, we propose a composite neighbor-aware convolutional metric network (CNCMN), aiming to learn each target's representation from its composite neighbors (i.e., both Euclidean and non-Euclidean neighbors) in a batchwise manner. Specifically, for each target in an HSI, its Euclidean neighbors are the pixels in the local square region centered on itself, and its non-Euclidean neighbors are several related nodes selected from the constructed full graph. Correspondingly, a composite convolution (CoConv) is proposed by coupling an image convolution and a graph convolution, which can perform flexible convolutions on those composite neighbors and extract adaptively fused features from them. Besides, to further boost classification, we also propose a mini-batch metric classifier to dynamically optimize interclass and intraclass distances of samples batch by batch, which is then combined with the CoConv to form the mini-batch CNCMN. Extensive experiments on three real-world HSIs demonstrate the advantages of the proposed method over mini-batch deep learning algorithms and have obtained the state-of-the-art performance in these fields. The code is available at: https://github.com/qichaoliu/HSI-CNCMN.

Subspace clustering (SC) has achieved remarkable success in hyperspectral images (HSIs) due to the powerful representation ability of handling high-dimensional complex data. However, most of the existing SC methods focus on linear subspace representation and ignore the more effective nonlinear representation. Besides, SC suffers from the bottlenecks, such as high computation load and memory capacity, due to the spectral decomposition of adjacency matrix for large HSIs. To overcome these limitations, we propose an end-to-end learnable network framework for large HSIs, called self-supervised deep multi-level subspace clustering (S2DMSC), which incorporates the convolutional neural network (CNN) module, multi-level subspace clustering (MSC) module, and high-quality pseudo-label-based self-supervised learning module into a unified learning framework. More concretely, the deep multi-level spatial-spectral representation from hierarchical superpixels is modeled as a sparsity-constrained self-expression module for SC to construct high-quality pseudo-labels to learn the network parameters and produce better clusters for hyperspectral pixels. Experimental results on four classical HSIs demonstrate the effectiveness of S2DMSC and exhibit superior clustering performance compared to the representative clustering methods.

Hyperspectral image (HSI) classification faces the inherent challenge of small sample learning, primarily due to the difficulty in labeling vast land covers. Meta-learning, with its ability to learn transferable meta-knowledge from existing HSIs, is seen as a promising solution. However, different HSIs usually have varying distributions manifested as differing spectral wavelengths and reflectance shifts, which is often neglected in existing methods, stalling the acquisition of transferable features. To address this issue, we introduce an attribute-driven spectral alignment (ADSA) method, which parameterizes and embeds spectral attributes (i.e., spectral wavelengths and reflectance shifts) into a domain-adaptation model, aiming to decouple the domain-specific attributes of different HSIs in an unsupervised manner. After training, the parameterized attributes of source-domain (SD) HSIs can be substituted with those of the target domain (TD), allowing the decoder of ADSA to rebuild new HSIs sharing identical spectral attributes. By this means, numerous distribution-consistent labeled samples preserving inherent spectral–spatial structures can be obtained. Then, a 3-D residual prototypical network (RPN) based on 3-D convolutions and metric learning is designed to model complex structures of HSIs, which in combination with the few-shot learning (FSL) framework can extract valuable discriminative knowledge from these auxiliary samples. Finally, by applying this learned knowledge to the TD HSI, only a small number of labeled samples are required to obtain satisfactory performance. Extensive experiments on four real-world HSIs demonstrate the effectiveness of our method, and the performance outperforms several state-of-the-art methods.

Sparse subspace clustering (SSC) is a representative data clustering paradigm that has been broadly applied in the unsupervised classification of hyperspectral images (HSIs). Existing SSC methods usually produce a subspace affinity matrix between representations of hyperspectral pixels first, followed by spectral clustering for the affinity matrix. To this end, the separated framework fails to exploit the dualities contained in both features and pixels or higher order entities at the same time, and thus, it is difficult to compute coclusters simultaneously. In addition, SSC methods often require expensive computational consumption and memory capacity to approximate the spectral decomposition of the affinity matrix, thus hindering the applicability of SSC methods for large HSIs. To overcome these limitations, we propose a novel graph convolutional sparse subspace coclustering (GCSSC) model with nonnegative orthogonal factorization for large HSIs in which affinity matrix learning and spectral coclustering are integrated into a unified optimizing model to obtain the optimal clustering results. Specifically, to form a more compact self-representation, the superpixel-based adaptive dictionary construction strategy is proposed instead of the global dictionary to precisely represent the pixels. To explore the spatial–contextual and spectral neighboring characteristics between dictionary atoms, graph convolution is incorporated into the dictionary atoms to aggregate the local neighborhood information, and the affinity matrix in the proposed coclustering framework is constructed under a joint sparsity constrained representation model. To reduce high computational consumption and memory capacity, a nonnegative orthogonal factorization constraint is proposed to offer an alternative spectral clustering for hyperspectral pixels and dictionary atoms simultaneously. The clustering performance of the proposed method is evaluated for three classical HSIs, and the experimental results illustrate that the proposed method is memory and computationally efficient and outperforms the state-of-the-art HSI clustering methods.

Clustering large hyperspectral images (HSIs) is a very challenging problem because large HSIs have high dimensionality, large spectral variability, and large computational and memory consumption. Recently, sparse subspace clustering (SSC) has achieved remarkable success in HSI clustering. However, most SSC-based methods suffer from the following bottlenecks for large HSIs: 1) high computational consumption and memory space during the construction of the similarity matrix and decomposition of the graph Laplacian matrix and 2) failure to capture the relationships among dictionary atoms, sparse coefficients, and hyperspectral pixels. To address these challenges, we propose a novel algorithm that extends SSC to cocluster large HSIs, called bipartite graph partition with graph nonnegative matrix factorization (BGP-GNMF). Specifically, to fully explore the characteristics of the spectral and spatial contexts in HSIs, we propose a novel superpixel and pixel coclustering framework with bipartite graph partitioning in the joint sparse representation domain, where superpixel-based dictionary atoms are defined as disjoint vertex sets of the bipartite graph and the joint sparsity representation is mapped into the adjacency matrix of the undirected bipartite graph. To overcome the challenges of high computational consumption and large memory space for large HSIs, the bipartite graph partition with orthonormal constrained nonnegative matrix factorization is proposed to simultaneously cluster the structured dictionary atoms and hyperspectral pixels with an indicator matrix. Finally, to exploit the intrinsic geometry of HSIs, we incorporate manifold regularization into the bipartite graph partition to improve final clustering accuracy. The effectiveness and efficiency of the proposed method are verified on three classical HSIs, and the experimental results illustrate the superiority of the proposed method compared with other state-of-the-art HSI clustering methods.

Clustering of hyperspectral images (HSIs) is a challenging task, due to high dimensional, unbalance data distribution and complex spectral-spatial structures. Existing methods mainly use either data spectral attribute only or spatial similarity only without gathering them appropriately. In this paper, under an extended linear discriminant analysis (LDA) framework, a new method, locally constrained collaborative representation based Fisher's LDA for clustering (LCR-FLDA), is proposed for the clustering of HSIs. We propose a New Fisher's LDA (FLDA) model by optimizing the integration of K-means-Laplacian and N-cut clustering to fully utilize the spectral-spatial discriminative representation information both in data and feature domain. Then, using locally constrained collaboration representation method to build a similarity matrix in FLDA to incorporate both local relationship structure and attribute information. Experimental results were conducted to illustrate the effectiveness of the proposed method.

Sparse subspace clustering (SSC) algorithm has achieved an impressive performances in hyperspectral images clustering. However, the raw samples contained noises were used to construct the dictionary. Moreover, SSC represented each signal individually ignoring the relationship among hyperspectral pixels. To overcome these problems, we propose a sparse dictionary learning method for hyperspectral images clustering, in which joint sparsity and shared Wavelets are integrated to improve the expressive power of the learnt dictionary. First, we incorporate the shared Wavelets as a base dictionary into a unified joint sparsity constrained optimizing model to learn a structured sparse dictionary from both spectral and contextual characteristics of hyperspectral images. Then, the sparse representation coefficients based on the learnt sparse dictionary are adopted to construct a non-negative affinity matrix of graph. Finally, spectral clustering is employed to the affinity matrix to obtain the final clustering result. Experimental results clearly demonstrate that the proposed algorithm outperforms other state-of-the-art methods on the hyperspectral dataset.

Limited and expensive availability of labeled training samples resulted in the development of methods defining the hyperspectral classification task in the form of data augmentation based supervised learning. However, most of the methods just implicitly utilize the spectral-spatial information in the isotropic neighborhood, instead of explicitly indicating the anisotropic or steering neighborhood system. In this paper, we apply steering stencils for estimating the local directional homogenous regions and exploiting more valuable spectral-spatial contexts. By using a best steering stencil matching method, we propose a data augmentation and refining method to improve the performance of any spectral-spatial classifier with limited labeled samples. Experiments show that the proposed method is very effective for many spectral-spatial classifiers.

Clustering for hyperspectral images (HSIs) is a very challenging task because HSIs usually have large spectral variability, high dimensionality, and complex structures. The main issue of this study is to develop an improved sparse subspace clustering (SSC) method for HSIs. As an extension of spectral clustering, SSC algorithm has achieved great success; however, the direct self‐representation dictionary which is created by raw samples has poor representation power and also the widely used dictionary learning (DL) such as K‐Singular Value Decomposition (K‐SVD) faces with the problems of high computational complexity. In this study, the authors propose a novel HSI clustering method based on sparse DL and anchored regression. The proposed method follows three stages: (i) sparse DL; (ii) anchored subspace construction and regression; and (iii) representation‐based spectral clustering. Specifically, we adopt a fast sparse DL method under a double sparsity constrained optimising model to capture the intrinsic HSIs. To establish a compact subspace for collaborative representation, we present an anchored subspace construction method by using atoms clustering and grouping methods. Owing to the anchored subspace, we can fast compute the representation coefficients with a predefined projection matrix. Experimental results demonstrate that the proposed method achieves the best performance for the HSIs clustering.

One significant advantage of the deep convolutional neural networks (DCNN) is their representational ability for local complex structures. Inspired by this observation, a DCNN based residual learning model is proposed to learn a nonlinear mapping function between the high-resolution (HR) and low-resolution (LR) image patches. The DCNN is trained based on image patches, which are only sampled from the HR/LR panchromatic (PAN) image without other training images. We train the DCNN to obtain a nonlinear mapping function with HR/LR PAN patch pairs using mini-batch gradient descent based on back-propagation. By assuming HR/LR multispectral (MS) image shares the same mapping function between HR/LR PAN image patches from the viewpoint of transfer learning methodology, the HR MS image can be reconstructed from the observed LR MS image using the trained DCNN. Owing to the advantage of the residual learning mechanism, the proposed method can achieve a good geometrical details injection while preserves the spectral features. Experimental results show that the proposed method provides a better performance in both visual perception and numerical measures compared with the conventional methods.

In this paper, a new heterogeneous neural networks based deep learning method, named HNNDL, is presented for supervised classification of hyperspectral image (HSI) with a small number of labeled samples. Specifically, a deep neural Network (DNN) and a convolutional neural network (CNN) are combined to build a HNNDL architecture. The proposed architecture contains three modules: 1) dimension reduction and feature extraction, 2) training pixel-wise DNN and CNN, 3) bilateral filtering based decision level fusion on two soft probability maps which is produced by above classifiers. The rationale behind this heterogeneous deep learning architecture is their ability to learn more abstract and robust local spectral-spatial information by taking full advantages of complementary ability of each networks, and thus boost the performance of HSI classifier. Experimental results on the widely used HSI indicate that the proposed approach outperforms several well-known classification methods in terms of classification accuracy.

In this paper, a novel spectral-spatial low-rank subspace clustering (SS-LRSC) algorithm is presented for clustering of hyperspectral images (HSI). Generally, employing the traditional LRSC framework directly cannot fully exploit the sample correlations in original spatial domain. Therefore, the proposed method utilizes a novel modulation strategy to modify the low rank representation matrix, which largely exploits the structure correlations. Specifically, a spectral and representation similarity weighted matrix is first applied to modulate the representation matrix; another local spatial bilateral filtering based modulation is further incorporated. Finally, the modulation method is integrated into the LRSC framework. Benefitting from the ability of the modulation method, SS-LRSC can capture both the structure correlations and inherent feature information of the data, which provides a competitive subspace clustering for HSI. Several experiments were conducted to illustrate the performance of the proposed algorithm.