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Satellite image classification is usually marked by several types of imperfection such as uncertainty, imprecision, and ignorance. Data fusion of additional sensors tries to overcome the types of imperfection by using probability, possibility, and evidence theories. Our approach will lead to improve classification accuracy of satellite images by choosing the optimum theory for a particular image context and proposing a theoretical framework based on a multiagent system and case-based reasoning. We validate our approach trough a set of optical images from the satellite Satellite Positioning and Tracking 4 and radar images from the European Remote Sensing satellite 2, and we show that the overall accuracy is considerably increased from 83% for maximum-likelihood classification applied to multispectral imagery to 94% with the proposed approach.