利用SVM的极化SAR图像特征选择与分类

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。

Feature Selection and Classification of Polarimetric SAR Images Using SVM

A new feature selection algorithm is presented using SVM, and then it is integrated into the classification procedure of polarimetric SAR images to construct a novel SVM-based classification method. In the novel method, the sequential backward selection strategy is used to search feature subsets, and the number of support vectors is taken as the estimation index. Compared with those using the initial feature set and the classical RELIEF-F algorithm, higher classification accuracy with less or equivalent number of features is observed in a wider range of SVM parameters using the novel method.