Unsupervised classification of POLSAR data based on the polarimetric decomposition and the co-polarization ratio

In this paper, a new classification scheme for polarimetric SAR data sets is presented. The proposed method mainly involves two concepts: Freeman-Durden decomposition and co-polarization ratio. The core concept of Freeman-Durden decomposition is to decompose the covariance matrix into three scattering mechanisms: surface scattering, double bounce scattering and volume scattering; the co-polarization radio represents the proportion of horizontal polarization and vertical polarization. The combination of these two characters can distinguish different vegetation types effectively. The proposed method mainly consists of three steps: first, apply Freeman-Durden decomposition to get the scattering characters; second, combine the scattering powers and co-polarization radio to divide the images into corresponding initial clusters; finally, improve the representation of each class, the data sets of which are classified by an iterative algorithm based on a complex Wishart density function. The effectiveness of this algorithm is demonstrated by two sets of data: NASA/JPL AIRSAR L-band data of San Francisco and Flevoland in The Netherlands by NASA/JPL AIRSAR in 1989.