Fast Shape Parameter Estimation of the Complex Generalized Gaussian Distribution in SAR Images
Xiangguang Leng, Kefeng Ji, Shilin Zhou, Xiangwei Xing
Abstract
Complex generalized Gaussian distribution (CGGD) is quite significant in synthetic aperture radar (SAR) modeling since original focused SAR data are complex-valued. However, the estimation method of the vital parameter of the CGGD, i.e., the shape parameter, is seldom studied. This letter proposes a fast shape parameter estimation method of the CGGD in SAR images. The proposed method is developed based on a concept in the complex signal processing field, i.e., complex signal kurtosis (CSK). Specifically, this letter provides an introduction to the CSK at first. Then, the relationship between the shape parameter and the CSK is elaborated. Finally, the estimation chain based on the relationship is proposed. Experimental results demonstrate that the proposed method outperforms the state-the-of-art, i.e., the maximum-likelihood (ML) method proposed by Novey et al. It works in a near-real-time fashion with good estimation precision, being much faster than Novey's method and achieving better performance in distinguishing different kinds of non-Gaussianity of typical SAR targets.