SAR Amplitude Probability Density Function Estimation Based on a Generalized Gaussian Model

Gabriele Moser (1) , Josiane Zerubia (2) , Sebastiano B. Serpico (1)

1 DIBE - Department of Biophysical and Electronic Engineering [Genoa]
2 ARIANA - Inverse problems in earth monitoring

In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed “method-of-log-cumulants” (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena.

Keywords: Generalized Gaussian (GG); parametric estimation; probability density function (PDF); synthetic aperture radar (SAR)

Domains: Image Processing [eess.IV]

Gabriele Moser, Josiane Zerubia, Sebastiano B. Serpico. SAR Amplitude Probability Density Function Estimation Based on a Generalized Gaussian Model. IEEE Transactions on Image Processing, 2006. ⟨inria-00561372⟩