Fully PolSAR image classification using machine learning techniques and reaction-diffusion systems

In this paper, we study the problem of supervised Fully PolSAR (polarimetric synthetic aperture radar) image classification. We estimate a complex Wishart model distribution for each class using training data, and we use such models to design a new classification procedure based on a diffusion-reaction equation. The method relies on simultaneously filtering and classifying pixels within the image. The diffusion term smooths the patches within the image, and the reaction term tends to move the pixel values towards the closest (in the sense of stochastic distances) representative class. We present a detailed study of the method accuracy using both simulated and true data, and we provide optimum parameters for its use. We show that the proposed method outperforms the results obtained using maximum likelihood and usual stochastic distance classification methods.