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Many studies have demonstrated that multiple classifier systems, such as random subspace method, obtain more outstanding and robust results than a single classifier. In this study, we propose a novel RSM framework which is composed of two parts. The first part is the construction of a weighted RSM, where weights are given by two classifier-based distributions. One is the feature weighting distribution, and the other is the subspace dimensionality distribution that helps for dynamically selecting the size of subspace with respect to the employed classifiers. The second part is to introduce the spatial information estimated by the Markov random filed theory into the Bayesian classifiers used in the framework. The real data experimental results show that the proposed framework obtains satisfactory performances, and the classification maps remarkably produce fewer speckles.