Anomaly and target detection by means of nonparametric density estimation

G. A. Tidhar, S. R. Rotman

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

7 Scopus citations

Abstract

We describe a novel completely non parametric high-dimension joint density estimation algorithm suited for anomaly and target detection using hyperspectral imaging. The new algorithm is compared against linear matched filter detection schemes with different available sample sizes, background statistics (MVN, GMM and non Gaussian). The new algorithm is shown to be superior in important cases.

Original languageEnglish
Title of host publicationAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
PublisherSPIE
ISBN (Print)9780819490681
DOIs
StatePublished - 1 Jan 2012
Externally publishedYes
Event18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery - Baltimore, MD, United States
Duration: 23 Apr 201227 Apr 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8390
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
Country/TerritoryUnited States
CityBaltimore, MD
Period23/04/1227/04/12

Keywords

  • Hyperspectral Imaging
  • Non parametric
  • Target detection

ASJC Scopus subject areas

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

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