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 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 OSP-GoDec, 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.

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.

Band selection (BS) is a crucial concept within the realm of remote sensing, involving the selection of the most suitable bands to accurately capture features of landforms and surfaces. Despite the promising results achieved by many existing methods, certain limitations remain. First, most methods rely on a single criterion for band evaluation, leading to an incomplete assessment and limited generalizability of bands. Second, there is a lack of emphasis on target detection thus some BS techniques commonly used for classification are less effective for detection. Therefore, this paper proposes MOBS-TD, a multi-objective optimization (MO)-based BS method specifically designed for target detection, which aims to select bands with better target separation and stronger robustness across various application scenes. Initially, we develop a MO model with three objectives and introduce a novel metric to quantify the target-background separability (TBS) of bands. Subsequently, a weighted similarity to ideal solution (WSIS) strategy is developed to clearly describe the dominance relations and strike a balance among multiple objectives in evolution. In addition, we devise an evaluation mechanism based on the ratio of maximum to sub-maximum (MSR) is devised for selecting the optimal solution from the Pareto front (PF), which has been empirically validated to be effective in reducing false alarms. Extensive experiments on real-world datasets demonstrate the competitiveness of MOBS-TD in remote sensing applications. The source code is available at https://github.com/sxdDlmu/MOBS-TD .

Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based on a spectral–spatial cross-dimensional attention network, named SSANet-BS. This network is comprised of three stages: a band attention module (BAM) that employs an attention mechanism to adaptively identify and select highly significant bands; two parallel spectral–spatial attention modules (SSAMs), which fuse complex spectral–spatial structural information across dimensions in HSI; a multi-scale reconstruction network that learns spectral–spatial nonlinear dependencies in the SSAM-fusion image at various scales and guides the BAM weights to automatically converge to the target bands via backpropagation. The three-stage structure of SSANet-BS enables the BAM weights to fully represent the saliency of the bands, thereby valuable bands are obtained automatically. Experimental results on four real hyperspectral datasets demonstrate the effectiveness of SSANet-BS.