Classification of interferometric SAR images based on parametric modeling in the fractional fourier transform domain

Abstract

In this paper, the importance of image transformation for parametric modeling of single-look complex (SLC) and in-terferometric SAR (InSAR) images is emphasized. For SLC images, the real and imaginary parts of the fractional Fourier transform (FrFT) coefficients have already been modeled with generalized Gaussian distribution (GGD). Here, this work is extended for InSAR images. The Kolmogorov-Smirnov (KS) test statistics show that FrFT simplifies the statistical response for both SLC and InSAR images, and helps to achieve more uniform KS statistics over all classes, which is important in order to model the whole database with a single distribution. Moreover, the classification of InSAR images with a feature vector composed of GGD parameters shows a performance comparable to that of a non-parametric feature vector.