Band selection (BS) is a vital technique for improving efficiency of hyperspectral image (HSI) processing. This letter proposes a dual-view structural similarity subspace clustering model (DVS3C) for BS. Traditional low-rank subspace clustering (LRSC) methods rely solely on single-view data (e.g., original HSI), potentially leading to the loss of critical information (e.g., spatial structures) and insufficient exploitation of the multi-dimensional features of HSI for optimal BS. To do so, DVS3C constructs a spatial view alongside the spectral view, leveraging global spectral-spatial information through subspace clustering to achieve complementary advantages between views. Besides, to overcome LRSC’s limitations in capturing band local structure, DVS3C introduces a structural similarity matrix to deeply exploit intraview neighborhood relationships of bands, further reducing band redundancy. Ultimately, an adaptive dual-view fusion strategy that iteratively optimizes a consensus matrix while dynamically adjusting the contribution of each view is designed to ensure view consistency. Experimental results on four public datasets demonstrate its remarkable stability and superiority. The source code is available athttps://github.com/ydk0912/DVS3C.

Contrastive learning has recently demonstrated great potential in hyperspectral image few-shot classification. However, conventional methods mainly emphasize instance-level similarity while neglecting class structure, often leading to the separation of intra-class samples. Moreover, the lack of explicit class prototype modeling often leads to ambiguous decision boundaries. To address these issues, this paper proposes a prototype-guided supervised momentum contrastive learning (PGSMC) for hyperspectral cross-domain few-shot classification. PGSMC first designs an asymmetric augmentation module to enhance sample diversity by applying distinct augmentation strategies and encoders to the query and key views. Subsequently, a momentum queue is employed to store historical key features and their corresponding labels. This mechanism enables smooth updates of class-level momentum prototypes and mitigates the limitations imposed by mini-batch training. Finally, a momentum prototype contrastive loss is formulated to guide the model toward class-level feature representations, thereby promoting more discriminative decision boundaries. Overall, PGSMC enables query samples to contrast with more representative momentum prototypes, enhancing inter-class separability and promoting well-defined class boundaries. Extensive experiments on five hyperspectral image datasets demonstrate that PGSMC significantly outperforms existing few-shot learning methods.

Abstract As one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface objects. Recently, many BS methods for target detection have achieved promising results by making full use of the priori spectral features of the target to be detected. Conversely, anomaly detection separates the anomaly based solely on the statistical distribution difference between anomaly and background without any prior information. Therefore, the development of BS for anomaly detection has lagged far behind that of BS for target detection. To this end, this paper proposes a novel BS algorithm dedicated to anomaly detection tasks, named anomaly‐background separation and particle swarm optimization (PSO)‐based BS. Specifically, an anomaly‐background separation framework (ABSF) is established to predetermine a priori knowledge of anomaly distribution. Then, three band prioritization criteria are constructed with the anomaly‐background constraints generated by ABSF. Finally, PSO is used to find the optimal subset of bands in the solution space. The experiments on two real datasets demonstrate that the proposed method yields better detection results and greater stability compared to other BS methods discussed in this paper.

Due to the redundancy and sparsity of hyperspectral data, sparse representation (SR) has proven to be well-suited for hyperspectral band selection (BS). Moreover, graph regularizers can effectively incorporate local structural information of the data to improve the solution of SR. However, existing unsupervised BS approaches typically consider only a simple graph based on a single spectral metric. In contrast, the hypergraph can capture the multiple adjacencies of the bands and has significant advantages. This letter proposes a hypergraph-regularized self-representation model (HyGSR) for BS. HyGSR is innovative in that it jointly combines the spectral similarity and band index as a new similarity metric to rationalize the local structure of bands extracted by hypergraph, while using a robustl2,1-norm to exploit the sparse properties of the data for BS. Experimental results on four real hyperspectral scenarios verify that HyGSR outperforms other competitors with high stability and usability.

Hyperspectral remote sensing image is rich in spectral information. Due to the limitations of sensors and the complexity of the scene, a large number of mixed pixels exist in the scene. Therefore, it is very necessary to develop the unmixing technology. The linear unmixing model and its derived algorithms have made some progress. The existing unmixing methods treat all pixels in the scene as mixed pixels for operation, but the real scene is often a complex scene with pure pixels and mixed pixels. Considering the unmixing of complex scenes, the robust linear unmixing model based on enhanced constraint of classification (ECRLU) is proposed in this paper. The model combines unmixing and classification. After extracting endmembers, the number of endmembers is expanded by using local similarity and spatial similarity to obtain hard classification items, so as to provide sparsity constraints for the model. In this paper, synthetic data set and real data set are used to verify the effectiveness of the model.

Abstract Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors. Therefore, for the application of hyperspectral images, unmixing technology is a key processing technology, such as linear mixing model and its derived algorithms have made a certain progress. However, a real scene often contains both pure and mixed pixels. The existing methods usually ignore the consideration and analysis of this situation in the process of model design and simulation experiment. In this context, this paper proposes a robust linear unmixing model with the enhanced constraint of classification for hyperspectral image. In general, it designs a framework combining unmixing and classification. In the task for real scene data, endmembers are extracted first, and then the hard classification term constructed after the expansion of endmembers (training samples) based on similarity is introduced to provide the sparsity constraint of the overall model, so as to realize relatively complete adjustment and effective image unmixing under complex conditions. Considering the scene with different distributions, the simulation experiment designs several groups of data tests, including different proportions of pure and mixing pixels. The unmixing results of three simulated datasets and two real datasets show that the unmixing results of this method are better than those of the other six comparison methods. This model improves the accuracy of unmixing and realizes effective unmixing.

Factors such as insufficient training samples, high-dimensional data features, and unbalanced data classes can degrade the accuracy of hyperspectral classification. To this end, this letter proposes an iterative training sample augmentation (ITSA) algorithm and a new classification model incorporating ITSA and maximum margin projection (ITSA-MMP). First, ITSA iteratively augments samples by a similar region clustering strategy (SRCS) integrating spatial-spectral metric. Then, box-plot for representative sample selection (BPRSS) is adopted to screen optimal samples for the final augmented sample set (ASS). Next, based on the ASS, MMP projects the hyperspectral image into a low-dimensional subspace to explore the local structure of the data manifold and improve the interclass separability of the data. Finally, the MMP-reduced data is classified by support vector machines. Experiments on two real hyperspectral datasets validate that ITSA-MMP can effectively increase the training sample set especially for small initial sample set and unbalanced dataset and obtain a higher classification accuracy.

This letter proposes an unsupervised band selection (BS) algorithm named residual driven BS (RDBS) to address the lack ofa prioriinformation about anomalies, obtain a band subset with high representation capability of anomalies, and finally improve the anomaly detection (AD). First, an anomaly and background modeling framework (ABMF) is developed via density peak clustering (DPC) to pre-determine the prior knowledge of the anomalies and background. Then, the DPC-based constraints are applied to R-Anomaly Detector (RAD), and three band prioritization (BP) criteria are derived to obtain the representative band subset for anomalies. Experiments on two datasets show the superiority of RDBS over other BS algorithms and verify that the obtained band subsets are strongly representative of anomalies.

This article presents a multispatial filtering module cascaded system (MSFMCS) for hyperspectral image classification (HSIC), which can serve as a paradigm to improve spectral–spatial classification. It includes multiple spatial filtering modules (SFMs) that are cascaded to particularly capture spatial information from the classification maps generated from the preceding modules. As a result, any spectral classifier (SC) can be used as an input to an initial/input module (IM). Through MSFMCS, its classification performance keeps improving as more SFMs are processed. To terminate MSFMCS, an automatic stopping rule is particularly designed by support vector machine (SVM) which is used not only as a classifier but also as a decision-maker. So, once an SC cannot be further improved, MSFMCS is terminated. One major benefit resulting from MSFMCS is its framework which can implement any arbitrary SC as its initial classifier in IM. Another is its ability in capturing additional spatial classification information module by module as the process progresses. A third one is no weights connected between modules so that no training phase is required like a feedforward neural network. Finally, the number of modules used in MSFMCS can be automatically determined by its stopping rule not predetermined empirically. To illustrate full advantages of MSFMCS in HSIC, three types of heterogeneous classifiers, pure-pixel-based SVM, mixed-pixel-based constrained energy minimization (CEM), and feature-extraction-based classifier—orthogonal total variation component analysis (OTVCA)—are used for experiments to demonstrate how MSFMCS can improve their classification performance.

A large number of spectral bands in hyperspectral data can help to identify ground objects but also bring additional computational burden. To address this issue, this letter proposes a new target-constrained band selection approach with particle swarm optimization (TCPSOBS) to select a more representational band subset with low redundancy for target detection. TCPSOBS obtains the local and global optimal values of the particle swarm in the current iteration process by calculating the fitness function of each particle derived by constrained energy minimization (CEM), and iteratively updates the particle swarm to find the particle with the optimal band subset index. Experiments prove that TCPSOBS can effectively improve the detection accuracy compared to other most advanced methods.

Iterative constrained energy minimization (ICEM) has shown success in classification. However, a drawback suffered from ICEM is its requirement of complete ground truth to calculate class means. This letter develops an iterative selection of training samples to extend ICEM with two versions: iterative fixed training sampling constrained energy minimization (CEM) (IFTS-CEM) which uses a fixed training sample set throughout the entire iterative process and iterative random training sampling CEM (IRTS-CEM) which uses a random training sampling (RTS) at each iteration. The experimental results demonstrate that IRTS-CEM performs better than IFTS-CEM and also comparable to ICEM.

Sparse representation (SR)-based models have shown to be a powerful category of frameworks for hyperspectral image classification (HSIC). However, current residual-driven methods mainly focus on the sparsity of the coefficient, which is generally used in conjunction with the dictionary. In fact, the discriminant information hidden behind the value of sparse coefficient is not fully exploited. In this letter, we analyze the SR-based framework from the perspective of sparse coefficient, develop the participation degree (PD)-driven decision mechanism, and establish a concise model called constraint representation (CR). Based on CR, an improved version called adjacent CR (ACR) is further proposed, with consideration of spatial coherence via adjacent constraint. Experimental results using two real hyperspectral datasets verify the improvements of the proposed methods over the other related models and their spatial variants.

Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order m and sparsity cardinality k. This article presents an orthogonal subspace-projection (OSP) version of GoDec to be called OSPGoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining p = m + j and k, the well-known virtual dimensionality (VD) is used to estimate p in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum orthogonal complement algorithm (MOCA) to estimate k. Consequently, LRaSMD can be realized by implementing OSP-GoDec using p and k determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.

Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for many applications in hyperspectral data exploitation. However, processing such high-dimensional data volumes that may result in redundant bands due to its high interband correlation will be a challenge. For target detection and classification, this is particularly true since there may only need a relatively small number of bands that respond one particular target of interest well, while most of other bands do not. Band selection (BS) is a major dimensionality reduction technique to remove the redundant bands and selects a few bands to represent the entire image. However, how to eliminate the effect of uninteresting targets with similar spectra on detection of interesting targets is a severe issue arising in target detection for BS. This article develops a new approach called target-constrained interference-minimized BS (TCIMBS) which can be used to select band subset for specific target detection, while annihilating targets of no interest and suppressing interferers and background. Its idea is derived from target-constrained interference-minimized filter (TCIMF). By taking advantage of TCIMF, two band prioritization (BP) criteria called forward minimum variance BP (FMinV-BP) and backward maximum variance BP (BMaxV-BP) along with their three band search-based BS counterparts called sequential forward TCIMBS (SF-TCIMBS), sequential backward TCIMBS (SB-TCIMBS), and improved SB-TCIMBS (SB-TCIMBS*) are derived. The experimental results suggest that TCIMBS can improve the detection accuracy and also achieve better performance in comparison with several state-of-the-art methods.

Low rank and sparsity matrix decomposition (LRaSMD) has received considerable interest lately. One of effective methods is called go decomposition (GoDec) which finds low rank and sparse matrices iteratively subject to a predetermined low rank order, m and a sparsity cardinality, k, In order to resolve issue of the empirically determined m and k, the well-known virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD) developed in maximum orthogonal complement algorithm (MOCA) are used for this purpose. The constrained energy minimization (CEM) is used for experiments to demonstrate that the GoDec with VD and MX-SVD performs very effectively.

Classification is a major task in hyperspectral image (HSI) processing. This paper develops an approach by taking advantage of low rank matrix derived from the low rank and sparse matrix decomposition (LRSMD) model which decomposes a hyperspectral data matrix X as X = L+S+n where L, S and n are referred to low rank, sparse and noise matrices respectively. The hyperspectral image classification (HSIC) is then performed on the low rank matrix L rather than the original data matrix X where the well-known go decomposition (GoDec) is used to produce such LRSMD model. To determine the two key parameters used in GoDec, the rank of L, m, and the cardinality of the sparse matrix, k the well-known virtual dimensionality (VD) and minimax-singular value decomposition (MX-SVD) methods are used for this purpose. Finally, to demonstrate advantages of using the low rank matrix L, support vector machine (SVM) and an edge-preserving filters (EPF)-based classifiers are implemented to evaluate classification performance.

Hyperspectral imagery provides wealthy spectral information to make it suitable for many applications. However, for specific applications, extracting suitable bands from high-dimensional data is a tedious and difficult task. In the past, many methods have been developed to perform band selection for specific tasks such as target detection. However, there is very little work to consider and deal with the effects of suspected interfering targets. In this paper, a new method for band selection, called target-constrained interference-minimized band selection (TCIMBS) is developed for specific target detection. It can select a band set with strong characterization capabilities for desired targets and good suppression for undesired targets and background (BKG). Experimental results demonstrate that TCIMBS can improve the detection performance, and also achieve better performances in comparison with several state-of-the-art methods.

Hyperspectral image classification (HSIC) faces three major challenging issues, which are generally overlooked. One is how to address the background (BKG) issue due to its unknown complexity. Another is how to deal with imbalanced classes since various classes have different levels of significance, particularly, small classes. A third one is fractional class membership assignment (FCMA) resulting from a soft-decision classifier. Unfortunately, the commonly used classification measures, overall accuracy (OA), average accuracy (AA), or kappa coefficient are generally not designed to cope with these issues. This article develops a 3-D receiver operating characteristic (3-D ROC) analysis from a detection point of view to explore how these three issues can be resolved for HSIC. Specifically, it first develops one-class classifier in BKG (OCCB), called constrained energy minimization (CEM), and multiclass classifier in BKG (MCCB), called linearly constrained minimum variance (LCMV) in conjunction with 3-D ROC analysis to address the BKG issue. Then, by considering a small class as a signal to be detected, its class accuracy can be interpreted as signal detection power/probability so that the 3-D ROC analysis can be used to address the imbalanced class issue. Finally, FCMA can be treated as a detector by converting a soft-decision classifier to a hard-decision classifier in such a manner that the 3-D ROC analysis is also readily applied. The experimental results demonstrate that 3-D ROC analysis provides a very useful evaluation tool to analyze the classification performance.

This paper presents a new band selection approach, called uniform band interval divided band selection (UBIDBS) which uniformly divides a band range into a finite number of band intervals from which a band can be selected from each band interval according to a custom designed band prioritization (BP) criterion. Two BP criteria are introduced. One is derived from orthogonal subspace projection (OSP). The other is based on correlation matrix R originated from constrained energy minimization (CEM). These two criteria allow users to identify a most significant band to be selected in each of band intervals. As a result, it avoids band decorrelation required by BP to remove adjacent high- correlated bands.

Background (BKG) is an integral part of an image and has significant effect and impact on hyperspectral image classification (HSIC). Unfortunately, how to address the BKG issue has not received much attention over the past years. This paper investigates this issue by developing a mixed pixel classifier, iterative constrained energy minimization (ICEM) and a posteriori classification measure, called precision (PR).

This paper presents a class information (CI)-based band selection (BS) approach to hyperspectral image classification (HSIC). It introduces a new concept from an information theory point of view, CI which can be used to determine an appropriate weight imposed on each class of interest. Specifically, two types of criteria, intraclass information criterion (IC) and interclass IC are derived as CI probabilities to measure CI that can be used to determine the number of training samples required to be selected for each class. With such CI-calculated probabilities, another new concept called class self-information (CSI) is also defined for each class that can be further used to define the class entropy (CE) so that CSI and CE can be used to determine the number of bands required for BS, nBS. In order to find desired nBS bands, two types of BS methods based on CSI and CE are custom-designed, called single class signature-constrained BS (SCSC-BS) which utilizes the constrained energy minimization (CEM) to constrain each individual class signature to select bands for a particular class according to its CSI-determined nBS and a multiple class signatures-constrained BS (MCSC-BS) which takes advantage of linearly constrained minimum variance (LCMV) to constrain all class signatures to select CE-determined nBS bands for all classes. These SCSC-BS and MCSC-BS selected bands are then used to perform classification and evaluated by CI-weighted classification measures by real image experiments. The results show that HSIC using judiciously selected partial bands as well as CI-weighted measures can improve HSIC with using full bands.