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Satellite imagery classification using the support vector machine (SVM) algorithm may be a time-consuming task. This may lead to unacceptable performances for risk management applications that are very time constrained. Hence, methods for accelerating the SVM classification are mandatory. From the SVM decision function, it can be noted that the classification time is proportional to the number of support vectors (SVs) in the nonlinear case. In this letter, four different algorithms for reducing the number of SVs are proposed. The algorithms have been tested in the frame of a change detection application, which corresponds to a change-versus-no-change classification problem, based on a set of generic change criteria extracted from different combinations of remote sensing imagery.