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To achieve real-time abundance estimation of hyperspectral images and improve the accuracy and efficiency of estimation, this article proposes a new band processing approach for abundance estimation, to be called sequential band fusion (SBF). To achieve SBF, a new band priority mechanism is proposed. It is derived from the concept of orthogonal subspace projection (OSP) by orthogonalizing undesired targets using projection while minimizing the variance resulting from the background. By taking advantage of OSP, the interfering effects caused by all undesired targets can be eliminated, and then, the detector produced by a target of interest can be further used as a measure of prioritizing bands as well as a means of searching bands for this particular target. As a result, two ranking-based band priority criteria (RP), called maximum OSP-based RP (MaxOSP-RP) and minimum OSP-based RP (MinOSP-RP), and two searching-based band priority criteria (SP), called sequential feedforward band search (SFBS) and sequential backward band search (SBBS), can be derived. We provide a detailed theoretical description and formula derivation of the SBF and combine it with band sequence (BSQ), RP, and SP to propose three different fusion mechanisms: SBF based on BSQ (SBF-BSQ), SBF based on RP (SBF-RP), and SBF with SP (SBF-SP) to make the fusion mechanism applied to different scenarios. Experimental results show that the proposed methods perform well for abundance estimation. [ABSTRACT FROM AUTHOR]

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]

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. [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]