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Texture features derived from Synthetic Aperture Radar (SAR) imagery using grey level co-occurrence matrix (GLCM) can result in very high dimensional feature spaces. Although this high dimensional texture feature space can potentially provide relevant class-specific information for classification, it often also results in over-dimensionality and ill-conditioned statistical formulations. In this work, we propose a polarization channel based feature grouping followed by a multi-classifier decision fusion (MCDF) framework for a levee health monitoring system that seeks to detect landslides in earthen levees. In this system, texture features derived from the SAR imagery are partitioned into small groups according to different polarization channels. A multi-classifier system is then applied to each group to perform classification at the subspace level (i.e., a dedicated classifier for every subspace). Finally, a decision fusion system is employed to fuse decisions generated by each classifier to make a final classification decision (healthy levee versus landslide in this work). The resulting system can handle the high dimensionality of the problem very effectively, and only needs a few training samples for training and optimization.