基于Cameron分解和SVM的极化SAR图像分类

Cameron分解先将极化散射矩阵分解为互易分量和非互易分量,再将互易分量进一步分解为对称分量和非对称分量,这是极化合成孔径雷达图像特征提取的有效途径。由四个分量的范数组成样本向量,运用基于统计学习理论的支持向量机设计分类器,提出了一种极化SAR图像分类算法,并对实测极化SAR数据进行分类实验。结果表明,将Cameron分解与SVM结合起来应用于极化SAR图像分类的算法是可行和有效的,通过选择不同的参数对分类结果影响很大,验证了参数选择在SVM分类器中的重要作用。

Classification of Polarimetric SAR Image Based on Cameron Decomposition and SVM

First,Cameron decomposition decomposes Sinclair matrix into reciprocity component and non-reciprocity component.Then,reciprocity component is decomposed into symmetric component and asymmetric component.This is a important way to extract properties from polarimetric synthetic aperture radar image.Samples are composed of norms of four components.Classifier can be designed using support vector machines based on statistical learning theory,a new algorithm of target classification is proposed,and classification experiments to polarimetric SAR data are done.The results indicate it is feasible and efficient to classify polarimetric SAR image by combining Cameron decomposition and SVM. Discrimination of classification results is rather big by selecting different parameters.Parameters selecting is very important to SVM classifier.