Qu Junrong, Qiu Xiaolan, Ding Chibiao, Lei Bin
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2021 Feb 12;21(4):1317. doi: 10.3390/s21041317.
Polarimetric synthetic aperture radar (PolSAR) image classification plays a significant role in PolSAR image interpretation. This letter presents a novel unsupervised classification method for PolSAR images based on the geodesic distance and K-Wishart distribution. The geodesic distance is obtained between the Kennaugh matrices of the observed target and canonical targets, and it is further utilized to define scattering similarity. According to the maximum scattering similarity, initial segmentation is produced, and the image is divided into three main categories: surface scattering, double-bounce scattering, and random volume scattering. Then, using the shape parameter α of K-distribution, each scattering category is further divided into three sub-categories with different degrees of heterogeneity. Finally, the K-Wishart maximum likelihood classifier is applied iteratively to update the results and improve the classification accuracy. Experiments are carried out on three real PolSAR images, including L-band AIRSAR, L-band ESAR, and C-band GaoFen-3 datasets, containing different resolutions and various terrain types. Compared with four other classic and recently developed methods, the final classification results demonstrate the effectiveness and superiority of the proposed method.
极化合成孔径雷达（PolSAR）图像分类在PolSAR图像解译中起着重要作用。本文提出了一种基于测地距离和K - Wishart分布的新型PolSAR图像无监督分类方法。通过计算观测目标与标准目标的肯纳矩阵之间的测地距离，并进一步利用该距离定义散射相似度。根据最大散射相似度进行初始分割，将图像分为三类主要散射类型：表面散射、二次散射和随机体散射。然后，利用K分布的形状参数α，将每个散射类别进一步细分为三个不同异质性程度的子类别。最后，迭代应用K - Wishart最大似然分类器更新结果，提高分类精度。对三个真实的PolSAR图像进行了实验，包括L波段机载合成孔径雷达（AIRSAR）、L波段星载合成孔径雷达（ESAR）和C波段高分三号数据集，这些数据集具有不同分辨率和各种地形类型。与其他四种经典及最新方法相比，最终分类结果证明了所提方法的有效性和优越性。
