Improving Log-Cumulant-Based Estimation of Heterogeneity Information in SAR Imagery
Jeová Farias Sales Rocha Neto, Francisco Alixandre Àvila Rodrigues · IEEE Geoscience and Remote Sensing Letters · 2023
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 theG0family of distributions, have been employed to SAR data and many of the current advancements in processing this imagery have been accomplished through extracting information from these models. In this paper, we propose improvements to parameter estimation inG0distributions using the Method of Log-Cumulants. First, using Bayesian modeling, we construct that regularly produce reliable heterogeneity estimates under bothG0AandG0Imodels. 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.