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In this paper, we propose the application of collective network of (evolutionary) binary classifiers (CNBC) to address the problems of feature/class scalability and classifier evolution, to achieve a high classification performance over full polarimetric SAR images even though the training (ground truth) data may not be entirely accurate. The CNBC basically adopts a “Divide and Conquer” type approach by allocating an individual network of binary classifiers (NBCs) to discriminate each SAR image class and performing evolutionary search to find the optimal binary classifier (BC) in each NBC. Such design further allows dynamic class and SAR image feature scalability in such a way that the CNBC can gradually adapt itself to new features and classes with minimal effort. Experiments demonstrate the classification accuracy and efficiency of the proposed system over the fully polarimetric AIRSAR San Francisco Bay data set.