A Novel Asymmetrical Probability Density Function for Modeling Log-Ratio SAR Images

Abstract

Multitemporal synthetic aperture radar (SAR) images are often employed for change detection use due to the stable and periodic monitoring of the ground by SAR sensors. Because of the multiplicative nature of speckle noise in SAR data, the comparison between multitemporal images is often carried out according to a standard log-ratio operator. Therefore, in this letter, the probability density function (pdf) of the log-ratio SAR image is studied. Assuming that the SAR speckle is gamma distributed with unit mean, we present the analytic distribution form of the log-ratio image. The distribution is capable of capturing the nonsymmetrical feature of the histogram of the log-ratio data. At the same time, we provide a moment-based method for estimating the parameters of the pdf using empirical data. In the experiments, we use chi-square goodness-of-fit test and one-sample Kolmogorov–Smirnov test to evaluate the performance of the approach. The Gaussian distribution and the generalized Gaussian distribution are also employed for comparison. Experiment on two real SAR data sets demonstrates the effectiveness of the proposed method.