Context-dependent fusion for mine detection using airborne hyperspectral imagery

Lijun Zhang, Hichem Frigui, Paul Gader, Jeremy Bolton

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

3 Scopus citations

Abstract

We present a method for fusing the decisions of multiple algorithms that use different hyperspectral imagery (HI) classification methods and apply it to mine detection. The proposed fusion method, called Cumulative Separation-Based (CSB) method, is embedded into our Context-Dependent Fusion for Multiple Algorithms(CDF-MA) framework. The CDF-MA is motivated by the fact that the relative performance of different algorithms can vary significantly depending on the type of the different targets and other environmental conditions. Results on real world HI data show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our initial experiments have also indicated that the proposed method outperforms all individual algorithms and the global weighted average fusion method.

Original languageEnglish (US)
Title of host publicationWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing
DOIs
StatePublished - Dec 21 2009
EventWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - Grenoble, France
Duration: Aug 26 2009Aug 28 2009

Publication series

NameWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

Other

OtherWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Country/TerritoryFrance
CityGrenoble
Period8/26/098/28/09

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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