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Large Margin GMM of Ranklets for Multispectral Image Classification Reda A. El-Khoribi Faculty of Computers and Information, Cairo University, 5 Zewail Street, Giza, Egypt This paper introduces an approach to supervised multispectral image classification. The approach uses discrete ranklet frame energy signature as feature inputs to large margin Gaussian mixture model (GMM) classifiers. Experimental results show that the proposed features have a considerably high discrimination power added to the power of large margin GMM discrimination. They also prove the superiority of the proposed features over both the wavelet based and pixel based features. Keywords: Large margin GMM, ranklet frames, multispectral image classification, Land cover classification.
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BibTex: @ARTICLE{PP1150834364, AUTHOR = {Reda A. El-Khoribi}, TITLE = {Large Margin GMM of Ranklets for Multispectral Image Classification}, JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing, GVIP}, YEAR = {2008},
VOLUME = {08}, ISSUE ={IV}, PAGES={13--18} }
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P1150834364
