Eigenvalue Analysis-Based Approach for POL-SAR Image Classification

A novel polarimetric synthetic aperture radar (POL-SAR) image classification approach is proposed in this paper by exploiting coherency matrix eigenvalues for polarimetric information representation and understanding. The approach consists of two parts. Initially, the statistical distributions of eigenvalue for homogeneous areas are analyzed by taking eigenvalues as the features of polarimetric information. The Bayesian classification method is applied to verify the feasibility of distinguishing different homogeneous areas. As a result, this method can work well those pixels with the similar scatter mechanism by using different polarimetric intensity information from eigenvalues. But this process cannot adequately distinguish those pixels with similar eigenvalues distribution. So, an eigenvalues-based local operator is defined to overcome the insufficient of the similar pixels by introducing a similar measure and eigenvalues-based texture information. After all pixels are classified by Bayesian classification, if the similarity of the pixel is larger than the given threshold, this pixel will be further classified by support vector machine using texture information. The proposed method is tested on three POL-SAR datasets, in which the average classification accuracy of eight categories for the Flevoland data from our method reaches nearly 90%.