Hyperspectral Target Detection Using Tree-Structured Probabilistic Graphical Model and Semi-Parametric Density Estimation

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

1 Scopus citations

Abstract

In this paper, we propose a novel semi-parametric target detector to be used within the general detection framework of the likelihood ratio test for the additive signal model. A tree-structured probabilistic graphical model is used to obtain lower dimensional representation of the background probability density function. The overall density estimation problem, which is reduced to finding univariate and bivariate estimates, is solved using the Gaussian mixture model. Target detection experiments are carried out using the hyperspectral airborne 'Viareggio 2013 trial' data set. The detector is shown to provide promising results for the detection of the targets of interest deployed in the scene.

Original languageEnglish
Title of host publication2018 9th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781728115818
DOIs
StatePublished - 1 Sep 2018
Event9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018 - Amsterdam, Netherlands
Duration: 23 Sep 201826 Sep 2018

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2018-September
ISSN (Print)2158-6276

Conference

Conference9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018
Country/TerritoryNetherlands
CityAmsterdam
Period23/09/1826/09/18

Keywords

  • Hyperspectral
  • probabilistic graphical models
  • semi-parametric density estimation
  • target detection

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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