Anomaly detection in non-stationary backgrounds

Nir Gorelnik, Hadar Yehudai, Stanley R. Rotman

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

22 Scopus citations

Abstract

In this paper, several algorithms are considered as solutions for detecting anomalies in images which are inherently nonstationary, i.e., the images contain more than one type of background. We conclude that a recent algorithm suggested by A. Schaum [1] is most successful when coupled with several variations which we suggest. In particular, in concurrence with Schaum, for pixels in transition zones between two neighboring stationary areas, it is crucial to choose or construct a covariance matrix which is appropriate for that particular area. Methods to choose both the sample covariance matrix and the estimated local mean will be discussed.

Original languageEnglish
Title of host publication2nd Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2010 - Workshop Program
DOIs
StatePublished - 29 Nov 2010
Event2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 - Reykjavik, Iceland
Duration: 14 Jun 201016 Jun 2010

Publication series

Name2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 - Workshop Program

Conference

Conference2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010
Country/TerritoryIceland
CityReykjavik
Period14/06/1016/06/10

Keywords

  • Hyperspectral
  • Subpixel point target detection

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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