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Remote Sens. 2018, 10(1), 113; https://doi.org/10.3390/rs10010113

Band Subset Selection for Hyperspectral Image Classification

1
,
1,2,*
and
1,3,4,5
1
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian 116026, China
2
State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
3
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
4
Department of Computer Science and Information Management, Providence University, Taichung 02912, Taiwan
5
Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
*
Author to whom correspondence should be addressed.
Received: 24 November 2017 / Revised: 4 January 2018 / Accepted: 10 January 2018 / Published: 15 January 2018
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Abstract

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. View Full-Text
Keywords: band selection (BS); band subset selection (BSS); hyperspectral image classification; linearly constrained minimum variance (LCMV); Otsu’s method; successive LCMV-BSS (SC LCMV-BSS); sequential LCMV-BSS (SQ LCMV-BSS) band selection (BS); band subset selection (BSS); hyperspectral image classification; linearly constrained minimum variance (LCMV); Otsu’s method; successive LCMV-BSS (SC LCMV-BSS); sequential LCMV-BSS (SQ LCMV-BSS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yu, C.; Song, M.; Chang, C.-I. Band Subset Selection for Hyperspectral Image Classification. Remote Sens. 2018, 10, 113.

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