Abstract
该文提出一种新的利用SVM的特征选择算法，并将其融入到极化SAR图像分类过程中，构成一种新的基于SVM的分类方法。其中，特征选择算法利用支持向量个数作为特征评估指标，并以顺序后退法作为搜索策略。真实数据的实验结果表明，该分类方法能有效降低SVM分类器对自身参数的敏感性，与利用原始特征集和经典的RELIEF-F的分类方法相比，该方法能以更少(或相当)的特征个数，在更广泛的SVM参数取值范围内获得更高的分类精度。
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