Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery
This work addresses SAR image understanding for remote sensing applications, but it is incremental as it builds on existing statistical models and estimation techniques.
The paper tackles the problem of speckle noise in Synthetic Aperture Radar (SAR) imagery by improving parameter estimation in $\mathcal{G}^0$ distributions using the Method of Log-Cumulants, resulting in reliable roughness estimates and a constant-time computation that is considerably faster than existing methods.
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 $\mathcal{G}^0$ 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 $\mathcal{G}^0$ distributions using the Method of Log-Cumulants. First, using Bayesian modeling, we construct that regularly produce reliable roughness estimates under both $\mathcal{G}^0_A$ and $\mathcal{G}^0_I$ 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.