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This article presents a new approach, called progressive band subset fusion (PBSF) for hyperspectral anomaly detection. Unlike band selection (BS) which selects bands according to band prioritization or band search strategies, PBSF fuses band subsets progressively during data collection processing. It is completely opposite to BS that must be done after data are acquired and then select bands by removing spectral redundancy as post-data processing. To accomplish PBSF, two versions of PBSF are derived: PBSF of the multiple-band subset (PBSF-MBS) and PBSF of uniform BS (PBSF-UBS). In particular, the fusion process takes place in an anomaly detector from a real-time processing perspective. Three approaches are developed to realize PBSF of two-band subsets simultaneously: PBSF-band sequential (PBSF-BSQ), PBSF-RT, and PBSF-zigzag. Extensive experiments demonstrate that PBSF has advantages over BS in many ways. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) provided with a substantial number of correlated bands causes calculation consumption and an undesirable “dimension disaster” problem for the classification. Band selection (BS) is an effective measure to reduce the information redundancy with the physics spectrum preserved for HSI. Although the existing BS methods have achieved noticeable progress, the correlation between neighbor bands still needs to be mined deeply for an effective selection criterion. This article proposes a BS approach to collecting the discriminative band subset for HSI classification (HSIC), which adopts the self-supervised learning paradigm to implement the BS by the auxiliary spectrum rebuilding (SR) task. In specific, we utilized a convolutional neural network (CNN) and a graph convolutional network (GCN) for the spectral–spatial feature extraction. Next, GCN and CNN are developed for the refinement of the band correlation sequentially. Afterward, the selected bands in terms of the acquired correlation are fed into the presented self-supervised SR network for spectral reconstruction. Simultaneously, the proposed architecture completed the selection with the optimization of the band reconstruction by a defined loss function. In this way, we supply substitution for selection criterion and path searching through the end-to-end framework. The extensive experimental results and analysis demonstrated that the proposed hybrid architecture provided a competitive band subset for the classification, and the accuracies with different types of classifiers are more effective than the compared BS methods. [ABSTRACT FROM AUTHOR]

This article proposes a new approach to hyperspectral band processing for anomaly detection, to be called sequential band fusion (SBF), derived from the band sequential (BSQ) data acquisition format used by a hyperspectral imaging sensor which fuses one single band at a time with previously fused band subset sequentially. In order to realize SBF, four versions, SBF-BSQ, initial band driven SBF (IBD-SBF), band prioritization SBF (BP-SBF), and band selection SBF (BS-SBF), are developed. Furthermore, to validate the sequentially fused results by SBF identical to that produced by combining all joint bands together, its mathematical theory and derivations are also presented. Finally, the full utility of SBF in anomaly detection is demonstrated through extensive experiments. [ABSTRACT FROM AUTHOR]

Anomaly detection (AD) generally finds targets that are spectrally distinct from their surrounding neighborhoods but cannot discriminate its detected targets one from another. It cannot even perform classification because there is no prior knowledge about the data. This paper presents a new approach to AD, to be called a posteriori AD for unlabeled anomaly classification where a posteriori indicates that information obtained directly from processing data is used as new information for subsequent data processing. In particular, a posteriori AD uses a Gaussian filter to capture spatial correlation of detected anomalies as a posteriori information which is included as new information for further AD. In doing so, a posteriori AD develops an iterative version of AD, referred to as iterative anomaly detection (IAD), which implements AD by feeding back Gaussian-filtered AD maps in an iterative manner. It then uses an unsupervised target detection algorithm to identify spectrally distinct anomalies that can be used to specify particular anomaly classes. To terminate IAD, an automatic stopping rule is also derived. Finally, it uses identified distinct anomalies as desired target signatures to implement constrained energy minimization (CEM) to classify all detected anomalies into unlabeled classes. The experimental results show that a posteriori AD is indeed very effective in unlabeled anomaly classification. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral–spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu’s method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. [ABSTRACT FROM PUBLISHER]