Direct estimation of vegetation parameters from covariance data in polarimetric SAR interferometry

Abstract

Polarimetric SAR interferometry (POLINSAR) is an emerging technique for the characterization of volumetric scattering processes. Each pixel of a POLINSAR interferogram is a 6/spl times/6 matrix of complex sample covariances among the polarimetric channels in the image pair. A model of polarimetric scattering from vegetation specifies the expected covariance matrix as a function of the vegetation parameters. The data matrix obeys a complex Wishart probability distribution that depends on the expected covariance. Using this, one can rind the maximum-likelihood estimate of the parameters from the data matrix. This paper presents the formula for the expected covariance matrix, as predicted by the model of Treuhaft and Siqueira for a random canopy over flat ground. An algorithm for computing the maximum-likelihood parameter estimate is derived. We test the algorithm on simulated data and compare its results to estimates derived from coherence samples. We conclude by discussing the extension of the direct estimation technique to more general POLINSAR scattering models.