POLSAR图像杂波L分布建模及其参数估计

Abstract

The statistical modeling based on the product model is an effective method to analysis high-resolution POLSAR images, whlie the core is the selection of texture model. The generalized gamma distribution (GΓD) is a generic model which includes Weibull distribution, Gamma distribution and Inverse Gamma distribution. So we can expect that if we modeling the texture component with GΓD, the product model will not only accurate, but also generally to the POLSAR images modeling. In this paper, we firstly derive the high moments and matrix log-cumulants (MLCs) of the GΓD random variables. Then we find that we can model the POLSAR images with L-distribution when the GΓD is applied to the texture component of the product model. Also we get a novel estimation based on the MLCs of L-distributed random variables. In this paper, a parameter estimation method based on the mixed moments is proposed, in order to solve the problem that the estimation based on matrix log-cumulants is invalid when the number of samples is small. What is more, the manifolds of theoretical MLCs for several distributions show the intrinsic relationship of the widespread distribution and L-distribution which can prove the universal of the latter. Finally, the correctness of modeling with L-distribution and the validity of the derivation of the estimator are verified though simulated data and real data.