Anomaly Detection with Training Data in Hyperspectral Imagery

Jun Liu, Yutong Feng, Weijian Liu, Danilo Orlando, Hongbin Li

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

Abstract

In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. We derive the generalized likelihood ratio test and obtain its analytical expressions of the probability of false alarm and probability of detection. The performance of the proposed detector is evaluated by using simulated and real data. The results demonstrate that this training data assisted detector outperforms its counterpart without training data.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Pages4836-4840
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Anomaly detection
  • generalized likelihood ratio test
  • hyperspectral imagery
  • training data

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