A Subpixel Target Detection Approach to Hyperspectral Image Classification

Bai, Xue; Yu, Chunyan; Wang, Yulei; Song, Ming; Li, Sen; Wang, Lin; Chen, Hsian‐Min; Chang, Chein‐I

doi:10.1109/tgrs.2017.2702197

Cited by 72 publications

(37 citation statements)

References 23 publications

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“…The precision rates produced by the four EPF-based methods were very low as also noted in [23,76]. However, ILCMV using bands selected by LCMV-BSS consistently performed very well in both P OA and P R .…”

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confidence: 80%

“…To address this issue, two additional measures, called precision rate, PR, and detection rate, PD (also known as recall rate), developed in [23,76] were introduced for HSIC where PR and PD have been widely used in pattern recognition such as medical imaging, handwritten character recognition, and biometric recognition. The definitions and details of POA, PR, and PD can be found in [23,76]. Tables 3-5 show PD, POA, and PR calculated by the ILCMV classification results in Figures 4-6 using the bands selected in Table 2 for Purdue's data, Salinas, and University of Pavia, respectively, where the best results with highest rates are shown in boldface.…”

Section: (A) Ground Truth (B) Full Bands (C) Ubs (D) Meac (E) Mdpmentioning

confidence: 99%

“…It has been shown in [23,76] that using overall accuracy (OA), P OA may not be sufficient to evaluate the effectiveness of classification performance. To address this issue, two additional measures, called precision rate, P R , and detection rate, P D (also known as recall rate), developed in [23,76] were introduced for HSIC where P R and P D have been widely used in pattern recognition such as medical imaging, handwritten character recognition, and biometric recognition. The definitions and details of P OA , P R , and P D can be found in [23,76].…”

Section: (A) Ground Truth (B) Full Bands (C) Ubs (D) Meac (E) Mdpmentioning

confidence: 99%

“…One is that the results of P D using full bands are already available in [23,76]. The other is that EPF-based methods using partial bands did not perform better than their counterparts using full bands.…”

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confidence: 99%

“…This is a crucial difference between ILCMV and existing hyperspectral image classification algorithms reported in the literature. For more details, we refer to [23,76]. Table 2 by seven BS methods-UBS, MEAC, MDPP, DSEBS, SQ LCMV-BSS-1, SQ LCMV-BSS-2, and SC LCMV-BSS, respectively-where the ground truth map and classification map produced by the full bands are also included in (a) and (b), respectively, for comparison.…”

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confidence: 99%

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Band Subset Selection for Hyperspectral Image Classification

Song

Chang

2018

Remote Sensing

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This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV) derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ) and successive (SC) algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

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confidence: 80%