Kernel principal component analysis for the construction of the extended morphological profile

M. Fauvel, J. Chanussot, J. A. Benediktsson

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

1 Citation (Scopus)

Abstract

Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remotesensing data. Features extracted using KPCA are used to construct the Extended Morphological Profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. The overall classification accuracy increases from 79% to 96% with the proposed approach.

Original languageEnglish
Title of host publication2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
PagesII843-II846
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa
Duration: 12 Jul 200917 Jul 2009

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2

Conference

Conference2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/TerritorySouth Africa
CityCape Town
Period12/07/0917/07/09

Other keywords

  • Hyperspectral data
  • Kernel principal component analysis
  • Morphological profile
  • SVM

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