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.
HMT, SVM, land cover classification, discriminative training.