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

Noga Karni, Yanir Goren, Stanley R. Rotman

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

1 Scopus citations

Abstract

Point target detection in hyperspectral data is often plagued by the inability to distinguish between the target and a (relatively) few false alarms. Even when, overall, the signal to noise ratio (SNR) to the overall data is good, the false alarms render use of many detection algorithms problematic. To solve this problem, we propose a two-step process for analyzing the data. We start by performing the standard matched filter (MF) algorithm. While the original covariance matrix is based on all the pixels in the hyperspectral cube, a second covariance matrix is constructed based on the highest detections. Running the algorithm a second time on the original data with this new covariance matrix, we distinguish between the targets and these background false detections. This new method was tested on real world test data and compared to traditional matched filter method results. In all cases, the new method showed a significant decrease in false alarms. Other benchmark metrics show the efficacy of this method.

Original languageEnglish
Title of host publicationAlgorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI
EditorsMiguel Velez-Reyes, David W. Messinger
PublisherSPIE
ISBN (Electronic)9781510635616
DOIs
StatePublished - 1 Jan 2020
EventAlgorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI 2020 - Virtual, Online, United States
Duration: 27 Apr 20208 May 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11392
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAlgorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI 2020
Country/TerritoryUnited States
CityVirtual, Online
Period27/04/208/05/20

Keywords

  • Covariance matrix.
  • False alarms
  • Hyperspectral imaging
  • Matched filter
  • Point target detection

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