Using pre-segmentation with the adaptive cosine estimator and matched filter algorithms for hyperspectral target detection

Ori Feldman, Dor Klinman, Stanley R. Rotman

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

Abstract

Previous studies have shown that, for certain data sets, segmentation can help target detection performance for the Matched Filter (MF) algorithm. In this paper, we study the implementation of clustering prior to the Adaptive Cosine Estimator (ACE) calculation and compare our results to the classic non-segmented ACE and Matched Filter algorithms. From our results, we conclude that the proposed algorithm improves Matched Filter results in low false alarm rate conditions, achieving higher accuracy and lower false alarms in target detection; the ACE algorithm results are only marginally affected by segmentation.

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

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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