In order to improve the accuracy of support vector machine (SVM) classification of remote sensing image, SVM parameter selection is an important part. In this paper, we analyze the influence of SVM parameters on classification performance. Aiming at the characteristics of particle swarm optimization (PSO) and genetic algorithm (GA) in optimization, a method of optimizing SVM parameters based on dynamic co-evolutionary algorithm (PSO-GA) is proposed. This method can dynamically adjust the selection probability of PSO and GA strategy, realize the complementarity of evolution between PSO and GA, improve the convergence speed and realize the optimization of depth and breadth. The experimental results show that the method improves the parameter selection efficiency of SVM, and the obtained parameters are optimal for the classification of the test samples.
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