Anomaly detection in hyperspectral imagery based on feature fusion of band subsets

Lin He, Quan Pan, Yongqiang Zhao, Jiwei Zheng, Kun Wei

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Detecting camouflaged targets in an unknown environment presents a great challenge in hyperspectral image analysis since the prior knowledge about targets and background is not available. A nomaly detection method for hyperspectral imagery was proposed for this problem. Features were extracted from subband sets of hyperspectral imagery, then fusion algorithm for detection was implemented by D-S evidence reasoning while basic belief assignment function was constructed involving high-order moments of features. Theoretical analysis and results of experiment verify the effectiveness of the algorithm.

Original languageEnglish
Pages (from-to)1752-1755
Number of pages4
JournalGuangzi Xuebao/Acta Photonica Sinica
Volume34
Issue number11
StatePublished - Nov 2005

Keywords

  • Band subsets
  • Evidence reasoning
  • Feature fusion
  • Hyperspectral imagery processing
  • Target detection

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