2023/06/22 by Jeová Farias Sales Rocha Neto, Neto, Jeova Farias Sales Rocha, Francisco Alixandre Àvila Rodrigues +1
Computer Science · Environmental Science · Mathematics · #Bayesian Methods and Mixture Models #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Soil Geostatistics and Mapping #Statistical Distribution Estimation and Applications
paper · pdf · doi:10.48550/arxiv.2306.13200
openalex publication_date 2023/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 through extracting information from these models. In this paper, we propose improvements to parameter estimation in G0 distributions using the Method of Log-Cumulants. First, using Bayesian modeling, we construct that regularly produce reliable roughness estimates under both G0A and G0I models. Second, we make use of an approximation of the Trigamma function to compute the estimated roughness 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 roughness information.