Title :
Kernel principal component analysis for the construction of the extended morphological profile
Author :
Fauvel, M. ; Chanussot, J. ; Benediktsson, J.A.
Author_Institution :
Lab. Jean Kuntsmann, INRIA Rhones Alpes, Grenoble, France
Abstract :
Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing 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.
Keywords :
feature extraction; geophysical image processing; image classification; principal component analysis; remote sensing; Extended Morphological Profile construction; Kernel Principal Component Analysis; SVM; classification accuracy; feature extraction; hyperspectral remote sensing; Data analysis; Data engineering; EMP radiation effects; Eigenvalues and eigenfunctions; Feature extraction; Hyperspectral imaging; Hyperspectral sensors; Kernel; Principal component analysis; Remote sensing; Morphological Profile; SVM; hyperspectral data; kernel principal component analysis;
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
DOI :
10.1109/IGARSS.2009.5418227
