Displacement Estimation by Maximum-Likelihood Texture Tracking

Olivier Harant, Lionel Bombrun, Gabriel Vasile, Laurent Ferro-Famil, Michel Gay
2011 IEEE Journal on Selected Topics in Signal Processing  
This paper presents a novel method to estimate displacement by maximum-likelihood (ML) texture tracking. The observed polarimetric synthetic aperture radar (PolSAR) data-set is composed by two terms: the scalar texture parameter and the speckle component. Based on the Spherically Invariant Random Vectors (SIRV) theory, the ML estimator of the texture is computed. A generalization of the ML texture tracking based on the Fisher probability density function (pdf) modeling is introduced. For random
more » ... variables with Fisher distributions, the ratio distribution is established. The proposed method is tested with both simulated PolSAR data and spaceborne PolSAR images provided by the TerraSAR-X (TSX) and the RADARSAT-2 (RS-2) sensors. Index Terms-Maximum-likelihood (ML), offset tracking, polarimetric synthetic aperture radar (SAR), spherically invariant random vectors, texture.
doi:10.1109/jstsp.2010.2100365 fatcat:jsmr2nerdbc7dbtkzjan7tqngy