Abstract:
Synthetic aperture radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle. Sop...Show MoreMetadata
Abstract:
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 by extracting information from these models. In this letter, we propose improvements to parameter estimation in \mathcal {G}^{0} distributions using the Method of Log-Cumulants (LCum). First, using Bayesian modeling, we construct the regularly produced reliable heterogeneity 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 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.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)