利用可分性指数的极化SAR图像特征选择与多层SVM分类

Polarimetric SAR image feature selection and multi-layer SVM classification using divisibility index

可分性指数(SI)可用来选择各类地物的有效分类特征,但在多维特征以及地物可分性较好的情况下,只利用可分性指数进行特征选择不能有效去除特征之间的冗余性。基于此,提出了利用可分性指数并辅以顺序后退(SBS)算法进行特征选择与多层支持向量机(SVM)分类的方法。首先,由各类地物在所有特征下的可分性指数选择分类地物和特征;然后,以该地物的分类精度为评估依据,利用顺序后退法筛选特征;其次,由剩余地物之间的可分性指数和顺序后退法依次选择各类地物的分类特征;最后利用多层SVM进行分类。实验结果表明,与只利用可分性指数选择特征进行多层SVM分类的方法相比,所提方法的分类精度提高了2%,各类地物的分类精度均高于86%,且运行时间为原来方法的一半。

Separability Index （SI） can be used to select effective classification features, but in the case of multi- dimensional features and good separability of geology, the use of separability index for feature selection can not effectively remove redundancy. Based on this, a method of feature selection and multi-layer Support Vector Machine （SVM） classification was proposed by using separability index and Sequential Backward Selection （SBS） algorithm. Firstly, the classification object and features were determined according to the Sis of all the ground objects under all the features, and then based on the classification accuracies of the objects, the SBS algorithm was used to select the features again. Secondly, the features of next ground objects were determined by the separability index of remaining objects and the SBS algorithm in turn. Finally, the multi-layer SVM was used for classification. The experimental results show that the classification accuracy of the proposed method is improved by 2% compared with the method of multi-layer SVM classification where features are selected only based on the SI, and the classification accuracy of all kinds of objects is higher than 86%, and the running time is half of the original method.