Anomaly detection with high resolution hyperspectral observations

Cécile Chenot, Mehrdad Yaghoobi, Mike E. Davies, Yoann Altmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Hyperspectral images enable the detection of targets due to the high spectral sampling. The latest generation of sensors also provides an unprecedented spatial resolution which is further exploited in this article to uncover hard to detect anomalies. In particular, we model and estimate the background building upon robust supervised linear unmixing. We benefit from the high resolution of the data to spatially constrain the background. This provides a novel framework for exploiting both the spectral and the energy variations created by the presence of unknown targets to detect them.
Original languageEnglish
Title of host publication2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
PublisherIEEE
Pages963-967
Number of pages5
ISBN (Electronic)9781728112954
DOIs
Publication statusPublished - 21 Feb 2019
Event6th IEEE Global Conference on Signal and Information Processing 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Conference

Conference6th IEEE Global Conference on Signal and Information Processing 2018
Abbreviated titleGlobal SIP 2018
CountryUnited States
CityAnaheim
Period26/11/1829/11/18

Keywords

  • Anomaly detection
  • Hyperspectral imaging
  • Linear mixture model

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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