散射模型与Wishart分类相结合的极化SAR图像分类

Classification of Polarimetric SAR Images with Scattering Model and Wishart Classifier

给出了一种散射模型与Wishart分类相结合的极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)图像非监督分类方法。首先利用去取向三分量散射模型进行粗分类,将像素划分为三种基本散射类型和混合散射类型;然后,在基本散射类型内根据占优散射机制的功率进行细分类,并根据Wishart距离对细分类的结果进行类别合并,合并到指定的类别数;最后对四种散射类型的像素分别重新进行Wishart迭代,从而实现极化SAR数据的非监督分类。利用美国AIRSAR机载系统采集的实测数据进行实验,并且同已有分类方法进行比较,结果表明本文方法改善了分类效果,且降低了体散射过估计。

This paper presents a new unsupervised classification of Pol SAR images with scattering model and Wishart classifier. Firstly,a three-component scattering model with deorientation was applied to classify Pol SAR images into three fundamental scattering categories and a mixed scattering category. Then,pixels in each kind of fundamental scattering categories were divided into more clusters based on the power of dominant scattering ones. After that,small clusters of each category were merged based on the Wishart distance between clusters. Finally,Pol SAR data was classified iteratively with Wishart classifier to achieve unsupervised classification results. Real polarimetric datas collected with U. S. AIRSAR system are used to verify the new scheme,experimental results show the new algorithm improves classification accuracy and lowers volume scattering overestimation compared with traditional classification.