Graph-based incomplete multi-view clustering algorithms have gathered much attention due to their impressive clustering performance. However, existing methods primarily leverage intra-view correlation from observed views, while ignoring the exploration of explicit compensation relationships between different views. Moreover, these methods need post-processing to get labels, and the separate steps lack negotiation, which may lead to sub-optimal solutions. To address these issues, we propose a Cross-view Anchor Graph Learning and Factorization (AGLF) method. AGLF develops an Anchor Graph Completion (AGC) framework that explicitly learn the missing subgraph structures. Instead of requiring post-processing, AGC directly produces soft labels. By establishing a third-order tensor of soft labels, it employs the tensor Schatten p-norm to enhance anchor graph learning and factorization. To significantly improve the quality of subgraph learning, AGLF incorporates compensation subgraphs from supplementary views into the AGC framework, enabling the construction of a better anchor graph for label learning. An optimization algorithm is devised to solve the objective function. Experimental results across various datasets demonstrate the effectiveness of our method.

Cross-scene hyperspectral image (HSI) recognition aims to assign a unique label to each pixel in the target scene by transferring knowledge from the source scene. Existing methods primarily rely on fully labeled source data and either partially labeled or unlabeled target data. No prior work has addressed the more challenging scenario of cross-scene recognition without label guidance in both scenes. To bridge this gap, we present the first study on cross-scene HSI clustering, proposing an anchor-guided discriminative subspace alignment and clustering (ADSAC) framework that follows a well-structured three-step learning paradigm to effectively mitigate distribution shifts. Specifically, we first develop an anchor-promoted graph learning (APGL) model to efficiently derive accurate clustering labels for the source scene by leveraging anchor-based structural information. Next, we propose a discriminative cross-scene subspace alignment (DCSA) model to improve feature discriminability and reduce distribution discrepancies. Finally, labels of the target scene are inferred after source clustering and cross-scene alignment. To solve the formulated models, we design tailored optimization algorithms to ensure high-quality learning. Extensive experiments demonstrate the superiority of the proposed framework over state-of-the-art methods.

Multi-view clustering of remote sensing data presents significant challenges, as it integrates diverse data representations to improve Earth observation. Although existing anchor graph-based methods have yielded promising results, they generally exhibit two key limitations: (1) the time-consuming process of directly exploring pixel clustering structures, and (2) insufficient modeling of high-order correlations among different views. To address these issues, we propose an Efficient Tensorized multi-view anchor graph clustering method with Affinity Propagation (ETAP) for remote sensing data. Based on superpixel preprocessing, anchor graphs are learned from view-specific pixels and anchors, while compressed anchor graphs are simultaneously learned from the view-specific anchors. An adaptive weighting scheme is introduced to facilitate the learning of these anchor graphs. To capture high-order correlations, tensor Schatten p-norm regularization is applied to the compressed anchor graphs. A connectivity constraint is introduced to uncover the clustering structures of anchors. Finally, pixel clustering structures are then efficiently revealed from the pseudo-labeled anchors through affinity propagation without requiring additional clustering steps. To solve the proposed formulation, we develop an alternating optimization algorithm. Extensive experiments on three public datasets demonstrate the efficacy and efficiency of the proposed method over state-of-the-art methods.

Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spectral clustering, as these methods often ignore guidance from spectral analysis; (2) Complex optimization processes require high computational burden, hindering scalability to large-scale problems; (3) Most methods do not address the rotational mismatch problem in spectral embeddings. To address these issues, we propose a highly efficient rotation-invariant spectral embedding (RISE) method for scalable incomplete multi-view clustering. RISE learns view-specific embeddings from incomplete bipartite graphs to capture the complementary information. Meanwhile, a complete consensus representation with second-order rotation-invariant property is recovered from these incomplete embeddings in a unified model. Moreover, we design a fast alternating optimization algorithm with linear complexity and promising convergence to solve the proposed formulation. Extensive experiments on multiple datasets demonstrate the effectiveness, scalability, and efficiency of RISE compared to the state-of-the-art methods.

Anchor-based incomplete multi-view clustering methods utilize anchors to uncover clustering structures. However, relying on anchor graphs for producing final indicators is indirect, which can lead to information loss and suboptimal outcomes. Besides, most methods neglect the potential of anchors for imputing missing views. To address these limitations, we propose a Joint View Imputation and Label Generation (JVILG) method. JVILG comprises the Anchor-based tensorized Label Generation (ALG) module for generating clustering labels and the Anchor-based sparse regularized Subspace Correlation (ASC) module for recovering missing views. The ALG module explicitly connects data observations, the fine-grained anchor matrix, and soft label matrices within a reconstruction framework through a membership matrix, while imposing tensor Schatten p-norm regularization on the constructed label tensor to capture spatial correlations among views. Meanwhile, the ASC module directly uses fine-grained anchors to impute missing data in respective views. By integrating the ALG and ASC modules, JVILG enhances synergy between different tasks and mitigates the impact of missing information on clustering. Experimental results on six datasets demonstrate the effectiveness of JVILG compared to both shallow and deep state-of-the art methods.The code is available at https://github.com/W-Xinxin/JVILG.

Pansharpening fuses lower-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) images that preserves both spatial and spectral information. Most deep pansharpening methods face challenges in cross-modal feature extraction and fusion, as well as in exploring the similarities between the fused image and both PAN and LRMS images. In this paper, we propose a spatial-spectral similarity-guided fusion network (S3FNet) for pansharpening. This architecture is composed of three parts. Specifically, a shallow feature extraction layer learns initial spatial, spectral and fused features from PAN and LRMS images. Then, a multi-branch asymmetric encoder, consisting of spatial, spectral and fusion branches, generates corresponding high-level features at different scales. A multi-scale reconstruction decoder, equipped with a well-designed cross-feature multi-head attention fusion block, processes the intermediate feature maps to generate HRMS images. To ensure HRMS images retain maximum spatial and spectral information, a similarity-constrained loss is defined for network training. Extensive experiments demonstrate the effectiveness of our S3FNet over state-of-the-art methods. The code is released at https://github.com/ZhangYongshan/S3FNet.

Multi-view multi-label learning (MVML) is a significant area of research in multimedia, providing a foundational framework for various real-world applications. However, the richness of its descriptive capabilities often results in high-dimensional data that contains redundant information, negatively affecting model performance. Most existing methods do not thoroughly explore the mapping of the distinctive parts while balancing common and distinctive information. Additionally, there has been limited focus on the relationships between different types of mappings and the high-order constraints among view-specific labels. In this paper, we propose a novel tensor-based method for view-specific label learning that integrates adaptive weight mechanisms into both global non-linear and local linear mappings. This method effectively captures high-order relationships among views and hybrid labels through hierarchical label correlation constraints. Central to our model is the ''Opposing yet Complementary'' procedure, which enhances feature weight representation at a finer-grained level. Extensive experiments on widely used multi-view multi-label datasets demonstrate significant performance improvements, underscoring the effectiveness of our proposed method.

The hyperspectral anomaly detection (HAD) aims to identify potential anomalies from complex backgrounds. Most reconstruction-based autoencoders equally treat background pixels and anomalies or ignore potential spatial information. In this letter, we propose an HAD method based on multiscale memory autoencoder and spatial filtering, abbreviated as SFM2AE. Specifically, by introducing memory modules into different hidden layers of the autoencoder, multiscale reconstruction of background and anomaly pixels is achieved in the spectral domain. In addition, morphological filtering in the spatial domain is used to extract spatial structural information from anomalies. Joint spatial-spectral anomaly detection is achieved by combining multiscale memory autoencoder and spatial filtering. Experiments demonstrate superior detection performance of the proposed method over the state-of-the-art methods.

Hyperspectral image (HSI) clustering has attracted increasing attention in recent years, because it doesn’t rely on labeled pixels. However, it is a challenging task due to the complex spectral-spatial structure. The emergence of large-scale HSIs introduces a new challenge in terms of heightened computational complexity. To address the above challenges, in this paper, we propose a structured anchor projected clustering (SAPC) model for large-scale HSIs. Specifically, we exploit spatial information reflecting in the generated superpixels to perform denoising and generate anchors. Based on the preprocessing, we simultaneously learn a pixel-anchor graph and an anchor-anchor graph in a projected feature space. Meanwhile, the rank-constraint is imposed on the Laplacian matrix related to the anchor-anchor graph. To uncover the clustering structure, we design a clustering inference strategy to propagate clustering labels from anchors to pixels based on the dual graphs. Additionally, we propose an efficient optimization strategy for the formulated SAPC model with linear time complexity in terms of the number of pixels. Since the anchor-anchor graph is with much smaller size, it is high efficient to obtain the structured anchors with pseudo labels. Thus, the clustering process is significantly accelerated. Extensive experiments on multiple large-scale HSI datasets demonstrates the superiority of our SAPC over the state-of-the-art methods. The source code is released athttps://github.com/ZhangYongshan/SAPC.

Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. While deep classifiers have achieved high predictive accuracy in this field, they lack the ability to rigorously quantify confidence in their predictions. This limitation restricts their application in critical contexts where the cost of prediction errors is significant, as quantifying the uncertainty of model predictions is crucial for the safe deployment of predictive models. To address this limitation, a rigorous theoretical proof is presented first, which demonstrates the validity of Conformal Prediction, an emerging uncertainty quantification technique, in the context of HSI classification. Building on this foundation, a conformal procedure is designed to equip any pre-trained HSI classifier with trustworthy prediction sets, ensuring that the true labels are included with a user-defined probability (e.g., 95%). Furthermore, a novel framework of Conformal Prediction specifically designed for HSI data, called Spatial-Aware Conformal Prediction (SACP), is proposed. This framework integrates essential spatial information of HSI by aggregating the non-conformity scores of pixels with high spatial correlation, effectively improving the statistical efficiency of prediction sets. Both theoretical and empirical results validate the effectiveness of the proposed approaches. The source code is available at https://github.com/J4ckLiu/SACP.

Multiview clustering task groups objects using multiple properties, such as RGB images, infrared images, and texture information. However, incomplete multi-view clustering faces significant challenges due to missing views that hinder clustering performance. This paper proposes a Discriminative Feature Recovery and Tensorized Matrix Factorization method (DFRTMF) that effectively recovers missing views, learns low-dimensional discriminative embeddings, and enables direct clustering. DFRTMF addresses high dimensionality through projection learning and enables the output of soft indicators. To improve projection and facilitate the recovery of missing views, we propose an uncorrelated constraint based on the scatter matrix of the recovered complete data, exploring the correlations between observed and missing views. To capture high-order correlations among views, a low-rank tensor constraint based on tensor Schatten p-norm regularization is applied to a third-order tensor composed of soft indicator matrices. DFRTMF adaptively controls the inter-coordination between these factorizations using view weights to optimally explore complementary information. Furthermore, we propose an alternating optimization algorithm based on the Alternating Direction Method of Multipliers to effectively solve the proposed objective function. Extensive experiments across diverse datasets demonstrate the effectiveness of DFRTMF compared to the state-of-the-art methods.

Hyperspectral image (HSI) clustering is challenging to partition pixels into different clusters due to the complex spatial distribution and high-correlated spectrum. Subspace clustering is a representative learning paradigm and has shown competitive performance in HSIs. Most existing methods ignore potential spatial or structural information and show difficulties in dealing with large-scale HSIs. In this paper, we propose an elastic graph fusion subspace clustering (EGFSC) framework that can flexibly incorporate spectral, spatial and structural information for large HSI clustering. Instead of performing pixel-level learning, superpixel-level learning is conducted according to the generated superpixels to lessen computation burden and memory cost. To explore structural information in two perspectives, a superpixel graph and a band graph are constructed based on the superpixel features. Considering the incompatible sizes of the two graphs, we present three effective dual graph fusion strategies to fuse them in different ways. With these graph fusion strategies, EGFSC is able to improve clustering performance by simultaneously considering spatial and structural information. To solve the proposed framework, we present a closed-form solution for easy implementation. Experiments demonstrate that the proposed EGFSC obtains 70.08%, 75.76%, 87.28% and 77.23% clustering accuracies on the four HSI datasets and outperforms the state-of-the-art methods. The source code is released athttps://github.com/ZhangYongshan/EGFSC.

Pansharpening of multispectral images aims to merge multispectral (MS) and panchromatic (PAN) images to produce high-resolution multispectral (HRMS) images. Unsupervised pansharpening algorithms, which are widely used in deep learning for pansharpening tasks, commonly employ generative adversarial networks (GANs). However, existing unsupervised methods based on GANs have some limitations: 1) restricted ability for joint preservation of spatial and spectral information, and 2) the receptive field of general convolutions restricts the extraction of long-range dependencies. To address these issues, we propose a frequency-guided generative adversarial networks for unsupervised pansharpening (FG-GAN). Our unsupervised framework uses high-frequency and low-frequency information as prior constraints to guide the training of the FG-GAN’s generator and discriminator networks, thereby enhancing the joint preservation of spatial and spectral details. Furthermore, a graph convolution-based generator network is designed, in which long-range edge dependencies are extracted and propagated by learning the relationships between distant edge feature nodes. Extensive experiments on the Quickbird and Gaofen-2 datasets demonstrate the effectiveness of our design: our method enhances spatial details and reduces the spatial distortion index (Ds) by 44.8%, while achieving the fidelity of spatial-spectral information with a HQNR score of 0.99.

Hyperspectral image (HSI) classification is a key task in the field of remote sensing, aiming to assign category labels to each pixel by leveraging the spectral and spatial information in HSIs. Recently, many deep learning (DL) methods, such as convolutional neural networks (CNNs) and Transformers, have been applied to this task, achieving significant results. However, most existing patch-based DL methods often overlook the potential relationships between the central pixel and its surrounding pixels. Additionally, the unique spectral characteristics of HSIs, such as the high correlation between adjacent spectral bands and low dependence between distant bands, also require special attention. Based on this, we propose a novel dual-branch convolution-Transformer network with spectral-spatial attention (CTSSA), which can effectively aggregate both local and global spectral-spatial features. Specifically, CTSSA comprises two core modules: the Pyramid Spectral Attention Module (PSAM) and the Center Transformer Encoder (CenterTE). The former extracts highly discriminative spectral features through a hierarchical multi-scale attention mechanism, capturing subtle differences between adjacent spectral bands. The latter improves the original Transformer encoder (TE) by introducing a center-attention mechanism to model the global relationship between the central pixel and its surrounding pixels, thereby enhancing classification accuracy while reducing computational complexity. Experimental results on four public datasets (Salinas, Pavia University, Houston, and WHUHi-LongKou) demonstrate that, compared with nine other networks, CTSSA achieves satisfactory performance with fewer parameters and relatively high efficiency.

Existing multiview clustering methods have achieved remarkable success for general images (GIs), but still have many limitations for clustering multimodal remote sensing images (RSIs). For example, these methods are sensitive to noise and spectral variability, ignore the diverse spatial structure information across modalities, or are computationally prohibitive for large-scale RSIs, thereby limiting their applications. This article proposes a multiscale spectral-spatial anchor graph fusion (MSSAGF) method for multimodal RSI clustering. MSSAGF develops a superpixel-based nonlinear neighborhood recovery strategy to reduce noise while enhancing spatial smoothness in multimodal RSIs. Using spatial-aware anchors to extract local spatial information for each modality, MSSAGF introduces multiscale local spectral-spatial anchor graphs to capture nonlinear correlations between the pixels and their corresponding local regions. A small number of anchors effectively reduces graph construction and partitioning costs, making the time complexity of MSSAGF nearly linear. This ensures that it is computationally feasible for large-scale RSIs. Finally, MSSAGF develops an adaptive fusion mechanism to fuse multiscale local anchor graphs into a unified global anchor graph, integrating complementary information across multiple modalities while directly obtaining the final clustering results. The experimental results on three multimodal RSI datasets demonstrate the superiority of our proposed method over state-of-the-art methods. Our code is publicly available athttps://github.com/W-Xinxin/MSSAGF.

Graph-based multi-view clustering has attracted remarkable attention due to its impressive performance. However, the typical framework consisting of graph learning and indicator generation may fail to align learned graphs with the underlying data structure due to the unidirectional pipeline from refined graphs to indicator generation. Another common problem is the inadequate prior information in graph learning methods. This paper proposes a Bidirectional Probabilistic Multi-graph Learning and Decomposition (BPMLD) method by establishing an explicit bidirectional pipeline between graph learning and indicator generation for multi-view clustering. Specifically, we design a confidence term based on clustering probability indicators and fuse it with graph learning to form clustering confidence driven graph learning. Meanwhile, graph tensor learning is introduced to recover the high-order correlations among the refined graphs. We further propose a multi-graph probability decomposition module to adaptively produce cluster indicators with probability representation from the refined graphs. The seamless integration between graph learning and indicator generation enables them to interact directly and enhance each other. To solve the proposed model, we design an effective optimization algorithm. Extensive experiments demonstrate the effectiveness of our method compared to state-of-the-art methods. The code is available at: https://github.com/W-Xinxin/BPMLD.

Anchor graph-based multiview clustering (AGMVC) demonstrates high efficiency and satisfactory performance. However, it still suffers from limitations such as single-structure similarity measurement, high time expenditure for large-scale anchor graph partitioning, and limited generalization ability. To alleviate the instability problem of single-structure information, this article proposes an anchor graph construction method that learns local and global (LG) structures simultaneously. To eliminate the need for graph partitioning and address the out-of-sample problem, we develop a landmark learning method to produce structural anchors, and further propose a pseudo-supervision affinity propagation (PSAP) framework. This framework jointly optimizes graph construction and landmark learning to disentangle the in-cluster distribution between samples and anchors while accelerating convergence. In addition, our framework introduces a clustering inference partition (CIP) strategy to directly output clustering results without the need for time-consuming postprocessing. Extensive experiments validate the efficiency and effectiveness of our framework. Our code is publicly available at https://github.com/W-Xinxin/PSAP.

Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most existing methods cannot simultaneously explore both structural and spatial information from global and local perspectives. In this letter, we propose a stacked graph fusion denoising autoencoder (SGFDAE) for hyperspectral anomaly detection. Specifically, the global and local graphs are constructed from an HSI to explore potential structural and spatial information. With the designed graph fusion strategy, an advanced graph denoising autoencoder with deep architecture is developed in a hierarchical manner. To achieve better reconstruction and detection, a greedy layerwise unsupervised pretraining strategy is presented for network training. Experiments show that SGFDAE achieves 97.17%, 98.43%, and 98.90% detection accuracies by averaging the results of the datasets from three different scenes and outperforms the state-of-the-art methods.

Band selection aims at selecting a subset of representative bands from original hyperspectral images (HSIs) to alleviate data redundancy. There are at least two issues existing in previous methods. First, most of them ignore global or local structural information without considering both two aspects. Second, the high-order correlations among spectral bands are not explored during learning. In this paper, we propose a tensorial global-local graph self-representation (TGSR) method for hyperspectral band selection. Specifically, we segment the HSI into diverse superpixels to show the inherent spectral-spatial structures. Based on the generated superpixels, we learn the global and local graphs to explore complex structural information from global pixels and local regions. To alleviate the computational burden, a transformation is designed for easy graph convolution of global graph and pixel spectral matrix. With global and local knowledge, we formulate a global-local graph self-representation model to conduct band correlation learning in a self-weighted manner. To explore the high-order correlations among bands, we reorganize the self-representation coefficient matrices into a tensor with low-rank constraint. We design an alternating optimization algorithm to solve the proposed model. The most representative band is selected from each band subset by performing spectral clustering on the constructed affinity matrix. Experiments on HSI datasets verify the effectiveness of our method over the state-of-the-art methods. The source code is released athttps://github.com/ZhangYongshan/TGSR.

Multimodal remote sensing image recognition aims to identify a category of land cover for every pixel with consistency and complementary information provided by different modalities. Most existing methods perform land cover recognition in a supervised manner with explicit label guidance. It is challenging to perform recognition without label guidance due to the complex spatial distribution and modality incompatibility, especially for large-scale data. In this article, we propose a dual graph learning affinity propagation (DGLAP) method for multimodal remote sensing image clustering. Based on the consistent spatial distribution from local regions, the proposed method learns an$N \times M$consensus anchor graph from N denoised pixels and M anchors by adaptive weighting different modalities along with projection learning. Meanwhile, an optimal$M \times M$compressed consensus anchor graph is learned from the updated anchors in different modalities with diverse adaptive contributions and connectivity constraint. Since$M \ll N$, clustering results can be efficiently obtained according to affinity propagation from the pseudolabeled anchors to the pixels without additional steps. An alternating optimization algorithm is devised to solve the proposed formulation. This is the first attempt to propose a ultraefficient graph-based clustering method with linear time complexity$\mathcal {O}(N)$and low time cost for large-scale multimodal remote sensing data. Extensive experiments on three datasets demonstrate the superiority of the proposed method over the state-of-the-art methods in both efficacy and efficiency. The code is released athttps://github.com/ZhangYongshan/DGLAP.

Multimodal remote sensing image recognition is a popular research topic in the field of remote sensing. This recognition task is mostly solved by supervised learning methods that heavily rely on manually labeled data. When the labels are absent, the recognition is challenging for the large data size, complex land-cover distribution and large modality spectrum variation. In this paper, a novel unsupervised method, named fast projected fuzzy clustering with anchor guidance (FPFC), is proposed for multimodal remote sensing imagery. Specifically, according to the spatial distribution of land covers, meaningful superpixels are obtained for denoising and generating high-quality anchor. The denoised data and anchors are projected into the optimal subspace to jointly learn the shared anchor graph as well as the shared anchor membership matrix from different modalities in an adaptively weighted manner to accelerate the clustering process. Finally, the shared anchor graph and shared anchor membership matrix are combined to derive clustering labels for all pixels. An effective alternating optimization algorithm is designed to solve the proposed formulation. This is the first attempt to propose a soft clustering method for large-scale multimodal remote sensing data. Experiments show that the proposed FPFC achieves 81.34%, 55.43% and 93.34% clustering accuracies on the three datasets and outperforms the state-of-the-art methods. The source code is released at https://github.com/ZhangYongshan/FPFC.

Hyperspectral image (HSI) clustering is challenging to divide all pixels into different clusters because of the absent labels, large spectral variability and complex spatial distribution. Anchor strategy provides an attractive solution to the computational bottleneck of graph-based clustering for large HSIs. However, most existing methods require separated learning procedures and ignore noisy as well as spatial information. In this paper, we propose a bipartite graph-based projected clustering (BGPC) method with local region guidance for HSI data. To take full advantage of spatial information, HSI denoising to alleviate noise interference and anchor initialization to construct bipartite graph are conducted within each generated superpixel. With the denoised pixels and initial anchors, projection learning and structured bipartite graph learning are simultaneously performed in a one-step learning model with connectivity constraint to directly provide clustering results. An alternating optimization algorithm is devised to solve the formulated model. The advantage of BGPC is the joint learning of projection and bipartite graph with local region guidance to exploit spatial information and linear time complexity to lessen computational burden. Extensive experiments demonstrate the superiority of the proposed BGPC over the state-of-the-art HSI clustering methods.

Hyperspectral image (HSI) classification aims at assigning a unique label for every pixel to identify categories of different land covers. Existing deep learning models for HSIs are usually performed in a traditional learning paradigm. Being emerging machines, quantum computers are limited in the noisy intermediate-scale quantum (NISQ) era. The quantum theory offers a new paradigm for designing deep learning models. Motivated by the quantum circuit (QC) model, we propose a quantum-inspired spectral-spatial network (QSSN) for HSI feature extraction. The proposed QSSN consists of a phase-prediction module (PPM) and a measurement-like fusion module (MFM) inspired from quantum theory to dynamically fuse spectral and spatial information. Specifically, QSSN uses a quantum representation to represent an HSI cuboid and extracts joint spectral-spatial features using MFM. An HSI cuboid and its phases predicted by PPM are used in the quantum representation. Using QSSN as the building block, we further propose an end-to-end quantum-inspired spectral-spatial pyramid network (QSSPN) for HSI feature extraction and classification. In this pyramid framework, QSSPN progressively learns feature representations by cascading QSSN blocks and performs classification with a softmax classifier. It is the first attempt to introduce quantum theory in HSI processing model design. Substantial experiments are conducted on three HSI datasets to verify the superiority of the proposed QSSPN framework over the state-of-the-art methods.

Hyperspectral image (HSI) clustering seeks to assign each pixel to a specific class without trained labels. This is a challenging task owing to the spatial and spectral complexity. Recently, anchor graph-based clustering has attracted considerable attention due to its flexibility in handling large-scale HSI data. However, these methods typically disregard noisy bands and require post-processing. To tackle these issues, we propose a structured-anchor projected clustering (SAPC) model for HSIs. In SAPC, the projection clustering is introduced into anchor graph learning to suppress noise, and the Laplacian rank constraints can quickly obtain the structure of anchors. Thus, we can directly obtain the clustering results through the anchor graph and the structured anchors. Moreover, we propose an iterative optimization method to efficiently solve the SAPC model. Extensive experiments show that our model achieves superior results.

Hyperspectral anomaly detection (HAD) aims to discern the objects deviated dramatically from their surrounding pixels. Some deep learning-based models integrating with the low-rank representation (LRR) have been proposed recently. The process of constructing dictionary in these methods is complex and the stability of the models is hard to maintain. To address these problems, in this paper, we propose a low-rank constrained memory autoencoder (LRMAE) for HAD. Specifically, we first train the memory autoencoder with a sparsity regularizer and a spectral consistency constraint in an unsupervised learning fashion and the embedded memory module is used to acquire the dictionary atoms for the construction of dictionary. The low-rank optimization process is conducted in the low-dimensional manifold space to obtain the final detection map. Substantial experiments are performed on three different datasets, and the final detection results demonstrate the superiority of the proposed model over other state-of-the-art methods.

Hyperspectral band selection has been proved to be effective in reducing redundant information for hyperspectral images (HSIs). Most existing band selection methods simply consider the relationship between bands by reshaping them into vectors and destroying the spatial structure. Moreover, the converted band vectors are usually high-dimensional, making the learning processing very time-consuming. To solve these problems, we propose a tensor decomposition based latent feature clustering (TDLFC) model for band selection. We maintain the tensor structure of the HSI and use CANDECOMP/PARAFAC (CP) decomposition to learn the latent low-dimensional representation of the bands to preserve spatial and spectral information. To avoid overfitting, we introduce a regularization term for the CP decomposition model. To solve the proposed model, we present an effective optimization algorithm as solution. Finally, the k-means algorithm is applied to the latent representation to get the band clustering results for band selection. Extensive experiments on three public HSI datasets show the superiority of our proposed model over the state-of-the-art methods.

Hyperspectral image (HSI) clustering has attracted great attention in the field of remote sensing. General anchor-based clustering methods often suffer from the problems of unstable anchor selection and insufficient utilization of spatial information, resulting in poor clustering performance. In this letter, a spectral-spatial superpixel anchor graph-based clustering (S3AGC) method is proposed for HSIs. Specifically, S3AGC further improves the clustering performance by simultaneously considering spatial and structural information as well as an advanced anchor selection strategy. Based on the spatial distribution, a useful HSI denoising solution is presented to reduce noise interference, and an effective anchor selection strategy is raised to alleviate the instability of random or clustering selection. Besides, we use a graph convolution method to embed structural information of spectral bands into the proposed framework. Experiments on HSI datasets verify the effectiveness of S3AGC.

Dimensionality reduction (DR) is important for feature extraction and classification of hyperspectral images (HSIs). Recently proposed superpixel-based DR models have shown promising performance, where superpixel segmentation techniques were applied to segment an HSI and then DR models like principal component analysis (PCA) or linear discriminant analysis (LDA) were employed to extract the local and/or global features. However, superpixelwise PCA based local features are unsatisfactory because PCA aims to extract features with high variance, which could be inefficient in superpixels with mixed objects or strong noise/outliers. In addition, superpixelwise unsupervised LDA based global features may neglect local (spatial-contextual) information. To address these issues, we propose a new spectral-spatial and superpixelwise unsupervised LDA (S3-ULDA) model for unsupervised feature extraction from HSIs. Specifically, the HSI is first segmented into various superpixels with pseudo labels. Then, superpixel based local reconstruction for HSI denoising is conducted. Next, superpixelwise unsupervised LDA (SuperULDA) is performed on both the original HSI and locally reconstructed data to extract global features. Then, superpixelwise unsupervised local Fisher discriminant analysis (SuperULFDA) is developed for local feature extraction, where each superpixel and its adjacent superpixels (along with their pseudo-labels) are fed into local Fisher discriminant analysis (LFDA) to extract local features. The superpixel-level local manifold structures can be effectively modeled by the proposed SuperULFDA. Finally, by fusing the extracted global and local features, novel global-local and spectral-spatial features can be obtained. Our experimental results on several benchmark HSIs demonstrate the superiority of the proposed method over state-of-the-art methods. The code of the proposed model is available at https://github.com/XinweiJiang/S3-ULDA.

Hyperspectral band selection aims to identify an optimal sub-set of bands from hyperspectral images (HSIs). Most existing methods explore the relationships between pair-wise pixels in a fixed graph. However, the quality of the initial fixed graph may be influenced by noises and user-defined parameters that may not be optimal for HSI analysis. In this paper, we pro-pose a graph learning based autoencoder (GLAE) to achieve unsupervised hyperspectral band selection. Using the relationships of pair-wise pixels within HSIs, GLAE constructs the initial graph to characterize the geometric structures of HSIs and then adjusts the graph to adapt the band selection process. To solve the proposed model, we intoduce an alternative optimization algorithm. Experiments and comparisons on three HSI datasets demonstrate that the proposed GLAE achieves better results over the state-of-the-art methods.

Superpixel based label propagation models have been successfully used for Hyperspectral Images (HSIs) classification especially when the training data are limited. However, it is inevitable that there are segmentation errors leading to data in one superpixel containing samples from different classes which could decrease the classification accuracy. In order to address this issue, we propose Superpixel Correction based Label Propagation for HSIs classification. First, superpixel segmentation technique is adopted to segment a HSI into many superpixel blocks. Then, clustering model density peak is used to adaptively cluster the data in each superpixel block to correct the segmentation errors. Finally, based on the corrected superpixel segmentation we construct global-local and spatial-spectral similarity graphs which results into effective propagation matrix for label propagation. The proposed model is verified in two HSIs data sets, and the experimental results demonstrate that the proposed model is superior to several state-of-the-art methods in terms of classification accuracy, especially in the case of limited training samples.

Hyperspectral images (HSIs) consisting of abundant spectral bands could lead to the curse of dimensionality issue when performing HSIs classification. In this letter, an unsupervised dimensionality reduction (DR) method termed Laplacian regularized collaborative representation projection (LRCRP) is proposed, where Laplacian regularization and local enhancement are introduced into collaborative representation (CR) to construct adjacent graph and then to reduce the spectral dimension in graph embedding framework. As the constructed graph simultaneously preserves the local manifold and global information in HSIs, the proposed LRCRP could be used to extract effective low-dimensional features for accurate HSIs classification. The experimental results on two HSI datasets demonstrate the effectiveness of the proposed model. The source code the proposed model is available athttps://github.com/XinweiJiang/LRCRP.

Hyperspectral image (HSI) clustering has attracted a great deal of attention, owing to lower cost and higher application prospects. Deep subspace clustering has been proved to be an effective method to explore the sample relationship of HSI clustering. However, due to the complex distribution of HSI data, the problem of data cluster overlap occurs frequently. In the actual sample distribution, a sample may belong to multiple subspaces. The complex sample distribution brings challenges to subspace clustering. In this letter, we propose a deep mutual information subspace clustering network (DMISC) to find a more intuitive feature space for non-linear subspace clustering. Technically, we maximize the mutual information between the samples and their generated features to enlarge the inter-class dispersion and intra-class compactness. The deep subspace method can find a more suitable non-linear intrinsic relationship, benefitting from the generated feature distribution. We evaluate DMISC on four HSI data sets and compare the performances with 12 popular clustering methods. The experiment results demonstrate our method outperforms many prior unsupervised methods.

Autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative representations with the assumption that connected samples share the same label and they cannot directly embed the geometric structure into feature extraction. To address these issues, in this paper, we propose a dual graph autoencoder (DGAE) to learn discriminative representations for HSIs. Utilizing the relationships of pair-wise pixels within homogenous regions and pair-wise spectral bands, DGAE first constructs the superpixel-based similarity graph with spatial information and band-based similarity graph to characterize the geometric structures of HSIs. With the developed dual graph convolution, more discriminative feature representations are learnt from the hidden layer via the encoder-decoder structure of DGAE. The main advantage of DGAE is that it fully exploits both the geometric structures of pixels with spatial information and spectral bands to promote nonlinear feature extraction of HSIs. Experiments on HSI datasets show the superiority of the proposed DGAE over the state-of-the-art methods. The source code of DGAE is available athttps://github.com/ZhangYongshan/DGAE.

As the most classical unsupervised dimension reduction algorithm, principal component analysis (PCA) has been widely used in hyperspectral images (HSIs) preprocessing and analysis tasks. Recently proposed superpixelwise PCA (SuperPCA) has shown promising accuracy where superpixels segmentation technique was first used to segment an HSI to various homogeneous regions and then PCA was adopted in each superpixel block to extract the local features. However, the local features could be ineffective due to the neglect of global information especially in some small homogeneous regions and/or in some large homogeneous regions with mixed ground truth objects. In this article, a novel spectral–spatial and SuperPCA (S3-PCA) is proposed to learn the effective and low-dimensional features of HSIs. Inspired by SuperPCA we further adopt superpixels-based local reconstruction to filter the HSIs and use the PCA-based global features as the supplement of local features. It turns out that the global–local and spectral–spatial features can be well exploited. Specifically, each pixel of an HSI is reconstructed by the nearest neighbors’ pixels in the same superpixel block, which could eliminate the noise and enhance the spatial information adaptively. After the local reconstruction-based data preprocessing, PCA is performed on each region and the entire HSI to obtain local and global features, respectively. Then we simply concatenate them to get the global–local and spectral–spatial features for HSIs classification. The experimental results on two HSIs data sets demonstrate the superiority of the proposed method over the state-of-the-art methods. The source code of the proposed model is available athttps://github.com/XinweiJiang/S3-PCA.

Unsupervised band selection is an essential step in preprocessing hyperspectral images (HSIs) to select informative bands. Most existing methods exploit the spatial information from the entire HSI while ignoring the difference between diverse homogeneous regions. Moreover, traditional methods utilize the limited size of data for model training that may result in degraded generalization performance. In this article, we propose a marginalized graph self-representation (MGSR) method for unsupervised hyperspectral band selection. To explore the spatial information from diverse homogenous regions, MGSR generates the segmentations of an HSI by superpixel segmentation and records the relationships between adjacent pixels of the same segmentation in a structural graph. Meanwhile, to improve the generalization and robustness, infinite corrupted samples are obtained from the original pixels by introducing noises in spectral bands for model training. To solve the proposed formulation, we design an alternating optimization algorithm to marginalize out the corruption and search for the optimal solution. Experimental studies on HSI datasets demonstrate the effectiveness of the proposed MGSR and the superiority over the state-of-the-art methods. The source code is available athttps://github.com/ZhangYongshan/MGSR.

Unsupervised band selection aims to select informative spectral bands to preprocess hyperspectral images (HSIs) without using labels. Traditional band selection methods only work well on Euclidean data, but ignore structural information of pixels and spectral bands. Moreover, they treat each HSI as a whole to exploit latent spatial information while ignoring the difference of spatial distribution between diverse homogeneous regions. In this paper, we propose a robust dual graph self-representation (RDGSR) method for unsupervised band selection. RDGSR uses superpixel segmentation technique to generate homogenous regions of each HSI to extract spatial information. Based on the segmentation result, the superpixel-based similarity graph and band-based similarity graph are constructed from HSIs to record spatial and structural information. With this knowledge, the dual graph convolution is developed and thel2,1-norm is introduced in the loss function and regularization term to eliminate the noise in rows for robust and effective band selection. The novelty of RDGSR is the joint utilization of the geometric structure of pixels with spatial consistency and the geometric structure of spectral bands to enhance the performance of band selection in a robustl2,1-norm manner. An iterative optimization algorithm is designed to solve the proposed formulation. Substantial experiments on HSI datasets are conducted to verify the superiority of the proposed RDGSR over the state-of-the-art methods. The source code is available at https://github.com/ZhangYongshan/RDGSR.

In image analysis, image samples from multiple sources may contain noisy features. Due to the difficulty of obtaining label information and complex intrinsic structures, performing unsupervised feature selection on multi-view data is a challenging problem. Most existing unsupervised multi-view feature selection methods may explore only the inter-view correlations at the view-level, and ignore the explicit correlations between features across multiple views. In this paper, we propose a tensor-based unsupervised multi-view feature selection (TUFS) method. Specifically, TUFS efficiently explores the full-order interactions among multi-view data without physically building a tensor. Besides, multiple local geometric structures for different views are constructed to facilitate unsupervised feature selection. To solve the proposed model, we design an alternating optimization algorithm. Experiments and comparisons on three image datasets demonstrate that the proposed TUFS yields better performance over the state-of-the-art methods.

In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However, multiple view data may include noisy, irrelevant and redundant features, while multiple class labels can be noisy and incomplete. Due to the special data characteristic, it is hard to perform feature selection on multi-view multi-label data. To address these challenges, in this paper, we propose a novel multi-view multi-label sparse feature selection (MSFS) method, which exploits both view relations and label correlations to select discriminative features for further learning. Specifically, the multi-labeled information is decomposed into a reduced latent label representation to capture higher level concepts and correlations among multiple labels. Multiple local geometric structures are constructed to exploit visual similarities and relations for different views. By taking full advantage of the latent label representation and multiple local geometric structures, the sparse regression model with an l2,1-norm and an Frobenius norm (F-norm) penalty terms is utilized to perform hierarchical feature selection, where the F-norm penalty performs high-level (i.e., view-wise) feature selection to preserve the informative views and the l2,1-norm penalty conducts low-level (i.e., row-wise) feature selection to remove noisy features. To solve the proposed formulation, we also devise a simple yet efficient iterative algorithm. Experiments and comparisons on real-world image datasets demonstrate the effectiveness and potential of MSFS.

Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC’s clustering accuracy.

Extreme Learning Machine (ELM) is an elegant technique for training Single-hidden Layer Feedforward Networks (SLFNs) with extremely fast speed that attracts significant interest recently. One potential weakness of ELM is the random generation of the input weights and hidden biases, which may deteriorate the classification accuracy. In this paper, we propose a new Memetic Algorithm (MA) based Extreme Learning Machine (M-ELM) for classification problems. M-ELM uses Memetic Algorithm which is a combination of population-based global optimization technique and individual-based local heuristic search method to find optimal network parameters for ELM. The optimized network parameters will enhance the classification accuracy and generalization performance of ELM. Experiments and comparisons on 22 benchmark data sets demonstrate that M-ELM is able to provide highly competitive results compared with other state-of-the-art varieties of ELM algorithms.