Estimation of the surface reflectivity of SAR images based on a marked Poisson point process model

This paper presents stochastic models and estimation algorithms for speckled images. We treat speckle from a novel point of view: that of a carrier of useful surface information as opposed that of a contaminating noise. The stochastic models for surface scattering are based on a doubly stochastic marked Poisson point process. For each of these surface scattering statistical models, we present estimation algorithms to determine the average surface reflectivity and scatterer density within a resolution cell using intensity measurements of speckled images. We show that the maximum likelihood estimator is optimal in the sense that the variance of the error is the smallest possible using any other conceivable estimate having the same bias with the same data.