Band Subset Selection for Anomaly Detection in Hyperspectral Imagery

Lin Wang, Chein I. Chang, Li Chien Lee, Yulei Wang, Bai Xue, Meiping Song, Chuanyan Yu, Sen Li

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


This paper presents a new approach, called band subset selection (BSS)-based hyperspectral anomaly detection (AD), which selects multiple bands simultaneously as a band subset rather than selecting multiple bands one at a time as the tradition band selection (BS) does, referred to as sequential multiple BS (SQMBS). Its idea is to first use virtual dimensionality (VD) to determine the number of multiple bands, nBS needed to be selected as a band subset and then develop two iterative process, sequential BSS (SQ-BSS) algorithm and successive BSS (SC-BSS) algorithm to find an optimal band subset numerically among all possible nBS combinations out of the full band set. In order to terminate the search process the averaged least-squares error (ALSE) and 3-D receiver operating characteristic (3D ROC) curves are used as stopping criteria to evaluate performance relative to AD using the full band set. Experimental results demonstrate that BSS generally performs better background suppression while maintaining target detection capability compared to target detection using full band information.

Original languageEnglish
Article number7946160
Pages (from-to)4887-4898
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number9
Publication statusPublished - 2017 Sept

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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