Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections

Idan Ben Shabat, Lihi Zinger, Stanley R. Rotman

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

Abstract

In image processing, the Matched Filter algorithm uses the estimated covariance matrix to give each pixel a score based on the similarity between the pixel and the signature of the target. While using this target detection algorithm, false alarms are inevitable. In order to solve this problem, a method using an iterative process to produce a second covariance matrix which only uses the most likely false alarms was presented [6]. In this paper, we test this method, attempt to improve it, and expand on the cases in which it is the most effective. In all cases, the new method showed a decrease in false alarms, and in some cases a decrease of over 85%.

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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