By Topic

On the Empirical-Statistical Modeling of SAR Images With Generalized Gamma Distribution

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Heng-Chao Li ; Key Lab. of Sci. & Technol. on Microwave Imaging, Chinese Acad. of Sci., Beijing, China ; Wen Hong ; Yi-Rong Wu ; Ping-Zhi Fan

In this paper, an efficient statistical model, called generalized Gamma distribution (GΓD), for the empirical modeling of synthetic aperture radar (SAR) images is proposed. The GΓD forms a large variety of alternative distributions (especially including Rayleigh, exponential, Nakagami, Gamma, Weibull, and log-normal distributions commonly used for the probability density function (pdf) of SAR images as special cases), and is flexible to model the SAR images with different land-cover typologies. Moreover, based on second-kind cumulants, a closed-form estimator for GΓD parameters is derived by exploiting the second-order approximation for Polygamma function. Without involving the numerical iterative process for solutions, this estimator is computationally efficient and, hence, can make the GΓD convenient for applications in the online SAR image processing. Finally, experimental results from tests carried out with actual SAR images demonstrate that the GΓD can achieve better goodness of fit than the state-of-the-art pdfs.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:5 ,  Issue: 3 )