提出了GA-SVM耦合用于高分遥感目标识别的特征优选方法, 将GA中的特征降维和适应度函数构建与SVM中的特征空间映射、样本训练以及分类结果在内容上耦合, 利用SVM的识别结果指导GA的进化方向。同时, 为减小未成熟收敛风险, 对传统GA做了改进。实验表明, 该方法在高分遥感影像目标识别中效果较好。

As one of the key techniques for high-resolution remote sensing target recognition, feature selection focused on how to find the critical features in the feature set to represent the target. Generally, the classical methods for feature selection were as follows, principal component analysis, empirical method, etc. When using these classical methods, recognition accuracy was not guaranteed. In this paper, a new method was proposed, the main idea of which was to couple GA (Genetic Algorithm) and SVM (Support Vector Machine) for feature selection, and using recognition results to guide the revolution direction of GA. Meanwhile, to reduce the risk of premature convergence of the traditional GA, some modification had been made. The experi-ment demonstrated the effectiveness of the proposed method.