基于Stokes矢量特征与GA-SVM的全极化SAR影像分类方法研究

Study on Full Polarimetric SAR Image Classification Method Based on Stokes vector features and GA-SVM

发展了一种基于极化散射特征的全极化SAR影像分类方法,探索了Stokes矢量特征作为分类特征的有效性,通过遗传算法耦合SVM的特征选取方法(GA-SVM)有效解决了分类器泛化不足的问题.以一景高分三号(GF-3)全极化影像作为主要的数据源,与同步外业调查获取的地面实况数据进行对比,结果表明所设计的待选分类特征集与特征选取方法得到的特征组合取得了较好的分类效果,总体精度达到90.00%,Kappa系数为0.87,影像部分地物的错分、误分现象得到改善.这表明:(1)GA-SVM的特征选取方法可以在有效地降低分类特征维度的同时提升目标SVM分类器的分类精度;(2)将Stokes矢量元素及其分解特征作为分类特征,可有效提升非参数模型分类的精度.

A new classification method of Polarimetric SAR image based on polarization scattering feature is developed,and the effectiveness of Stokes vector feature as a classification feature is explored,and the method of feature selection（GA-SVM）with genetic algorithm coupled with SVM effectively solves the problem of insufficient generalization ability of classifier.It provides a new idea for the classification of polarimetric SAR images based on polarization scattering characteristics.The Full Polarization SAR image of GF-3 was used as test data,and the effectiveness of the method was validated by the ground data obtained by the field survey.The results show that the feature combination chosen from the feature set designed in this paper by GA-SVM method achieved good classification result,the overall accuracy reaches the 90.00% and the Kappa coefficient is 0.87.The main conclusions of this study as follows：（1）The feature selection method of GA-SVM can improve the classification accuracy of the target SVM classifier while effectively reducing the classification feature dimension;（2）the Stokes vector element and its decomposition feature can be used as the classification feature to effectively enhance the accuracy of nonparametric model classifier.