Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 783194 14 pages doi 2009 783194 Research Article Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas Mathieu Fauvel 1 2 Jocelyn Chanussot 1 and Jon Atli Benediktsson2 1 GIPSA-lab Grenoble INP BP 46 38402 Saint Martin d Heres France 2 Faculty of Electrical and Computer Engineering University of Iceland Hjardarhagi 2-6 107 Reykjavik Iceland Correspondence should be addressed to Mathieu Fauvel Received 2 September 2008 Revised 19 December 2008 Accepted 4 February 2009 Recommended by Mark Liao Kernel principal component analysis KPCA is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experiment kernel principal components 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. For the one data set the overall classification accuracy increases from 79 to 96 with the proposed approach. Copyright 2009 Mathieu Fauvel et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction Classification of hyperspectral data from urban areas using kernel methods is investigated in this article. Thanks to recent advances in hyperspectral .
