Improving Log-Cumulant-Based Estimation of Heterogeneity Information in SAR Imagery

Synthetic aperture radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle. Sophisticated statistical models, such as the G0 family of distributions, have been employed to SAR data and many of the current advancements in processing this imagery have been accomplished by extracting information from these models. In this letter, we propose improvements to parameter estimation in G0 distributions using the Method of Log-Cumulants (LCum). First, using Bayesian modeling, we construct the regularly produced reliable heterogeneity estimates under both G0A and G0I models. Second, we make use of an approximation of the Trigamma function to compute the estimated heterogeneity in constant time, making it considerably faster than the existing method for this task. Finally, we show how we can use this method to achieve fast and reliable SAR image understanding based on heterogeneity information. © 2004-2012 IEEE.