Class Information-Based Band Selection for Hyperspectral Image Classification

Meiping Song, Xiaodi Shang, Yulei Wang, Chunyan Yu, Chein I. Chang

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

This paper presents a class information (CI)-based band selection (BS) approach to hyperspectral image classification (HSIC). It introduces a new concept from an information theory point of view, CI which can be used to determine an appropriate weight imposed on each class of interest. Specifically, two types of criteria, intraclass information criterion (IC) and interclass IC are derived as CI probabilities to measure CI that can be used to determine the number of training samples required to be selected for each class. With such CI-calculated probabilities, another new concept called class self-information (CSI) is also defined for each class that can be further used to define the class entropy (CE) so that CSI and CE can be used to determine the number of bands required for BS, nBS. In order to find desired nBS bands, two types of BS methods based on CSI and CE are custom-designed, called single class signature-constrained BS (SCSC-BS) which utilizes the constrained energy minimization (CEM) to constrain each individual class signature to select bands for a particular class according to its CSI-determined nBS and a multiple class signatures-constrained BS (MCSC-BS) which takes advantage of linearly constrained minimum variance (LCMV) to constrain all class signatures to select CE-determined nBS bands for all classes. These SCSC-BS and MCSC-BS selected bands are then used to perform classification and evaluated by CI-weighted classification measures by real image experiments. The results show that HSIC using judiciously selected partial bands as well as CI-weighted measures can improve HSIC with using full bands.

Original languageEnglish
Article number8765783
Pages (from-to)8394-8416
Number of pages23
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number11
DOIs
Publication statusPublished - 2019 Nov

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences

Fingerprint

Dive into the research topics of 'Class Information-Based Band Selection for Hyperspectral Image Classification'. Together they form a unique fingerprint.

Cite this