Improved covariance equalization for change detection in hyperspectral images

Nofar Piri, Eden Nisanov, Erez Yardeni, Stanley R. Rotman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper will focus on analyzing the Covariance Equalization (CE) change detection algorithm for hyperspectral data. We analyze its weaknesses and suggest a method to improve the algorithm using normalization.

Original languageAmerican English
Title of host publicationAlgorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII
EditorsMiguel Velez-Reyes, David W. Messinger
PublisherSPIE
ISBN (Electronic)9781510642911
DOIs
StatePublished - 1 Jan 2021
EventAlgorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII 2021 - Virtual, Online, United States
Duration: 12 Apr 202116 Apr 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11727

Conference

ConferenceAlgorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/04/2116/04/21

Keywords

  • Covariance Equalization
  • Hyperspectral
  • Hyperspectral image
  • Target detection

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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