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Support Vector Machine Training of HMT Models for Land Cover Image Classification Reda A. El-Khoribi Faculty of Computers and Information, Cairo University, 5 Zewail Street, Giza, Egypt This paper introduces a novel approach to supervised classification of multispectral images. The approach uses a new discriminative training algorithm for discrete hidden Markov tree (HMT) generative models applied to the multi-resolution ranklet transforms. System is implemented and tested on a set of Landsat 7-band images containing eight different land cover classes. Experimental results of the system show significant improvement over the baseline HMT system and give a superior performance in land cover classification. Keywords: HMT, SVM, land cover classification, discriminative training.
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BibTex: @ARTICLE{PP1150834318, AUTHOR = {Reda A. El-Khoribi}, TITLE = {Support Vector Machine Training of HMT Models for Land Cover Image Classification}, JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing, GVIP}, YEAR = {2008},
VOLUME = {08}, ISSUE ={IV}, PAGES={7--11} }
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P1150834318
