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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]

When predicting data for which limited supervised information is available, hyperspectral target detection methods based on deep transfer learning expect that the network will not require considerable retraining to generalize to unfamiliar application contexts. Meta-learning is an effective and practical framework for solving this problem in deep learning. This article proposes a new meta-learning based hyperspectral target detection using Siamese network (MLSN). First, a deep residual convolution feature embedding module is designed to embed spectral vectors into the Euclidean feature space. Then, the triplet loss is used to learn the intraclass similarity and interclass dissimilarity between spectra in embedding feature space by using the known labeled source data on the designed three-channel Siamese network for meta-training. The learned meta-knowledge is updated with the prior target spectrum through a designed two-channel Siamese network to quickly adapt to the new detection task. It should be noted that the parameters and structure of the deep residual convolution embedding modules of each channel in the Siamese network are identical. Finally, the spatial information is combined, and the detection map of the two-channel Siamese network is processed by the guiding image filtering and morphological closing operation, and a final detection result is obtained. Based on the experimental analysis of six real hyperspectral image datasets, the proposed MLSN has shown its excellent comprehensive performance. [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]

[Display omitted] • Fluorescent sensors that can simultaneously detect multiple targets are described. • The applications of these sensors in pathological processes are summarized. • Both molecular sensors and nanosensors are outlined. • Sequential sensors are separately described. The visualization of diseases is a crucial goal for clinicians as it aids in accurate diagnosis and clinical surgery. In recent years, fluorescence sensors that exhibit optical changes in response to biological events have been considered as an alternative or complementary tool for disease diagnosis and surgery. Particularly, sensors that can differentiate between two or more targets in different emission channels are preferred as they provide more information about the interplay of relevant targets in a specific pathological process. This enables a deeper understanding of pathogenesis investigation and superior accuracy in disease diagnosis. This review provides an informative summary of the development of fluorescent sensors for multiple targets in different emission channels. The review introduces sensors based on molecular and nano platforms, emphasizing their optical changes and bioimaging applications. Importantly, the achievements, challenges, and perspectives in this field will be highlighted. [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]

This paper develops a new Neyman–Pearson detection approach, to be called band-specified virtual dimensionality (BSVD), to estimating the number of bands required by band selection (BS), $n_{\mathrm {BS}}$ , as well as finding desired bands at the same time. Its idea is derived from target-specified virtual dimensionality (TSVD) where targets under hypotheses as signal sources in TSVD are replaced with bands as signal sources and the test statistics derived for a Neyman–Pearson detector (NPD) is signal-to-noise ratio (SNR) that is used to derive orthogonal subspace projection (OSP) approach for hyperspectral image classification and dimensionality reduction. Accordingly, the resulting virtual dimensionality is referred to as OSP-based BSVD. Several benefits resulting from BSVD cannot be offered by the traditional BS methods. One is its direct approach to dealing with $n_{\mathrm {BS}}$. Another is no-search strategy needed for finding optimal bands. Instead, it uses NPD to determine and rank desired bands for band prioritization. Most importantly, it determines $n_{\mathrm {BS}}$ and finds desired bands simultaneously and progressively. [ABSTRACT FROM AUTHOR]

Despite the fact that many approaches to hyperspectral image classification are reported, specifically spectral–spatial based methods, this paper presents a rather different approach from a viewpoint of mixed pixel classification, referred to iterative target-constrained interference-minimization classifier (ITCIMC), which includes an iterative Gaussian filtered feedback process to capture the spatial contextual information so as to improve hyperspectral image classification for multiple classes at one-shot operation. In order to evaluate classification performance more effectively, new performance measures other than commonly used overall accuracy (OA) are introduced, particularly, precision rate (PR), misclassification (MC) rate which have been overlooked in hyperspectral image classification. To illustrate the differences among OA, MC rate, and PR, two concepts of a priori classification and a posteriori classification are also proposed from a statistical signal processing point of view. As shown by experiments, ITCMC generally performs significantly better than the existing spectral–spatial hyperspectral image classification techniques in terms of PR and MC rate at the expense of slight loss of OA. [ABSTRACT FROM PUBLISHER]

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]