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Displacement Estimation by Maximum-Likelihood Texture Tracking

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5 Author(s)
Harant, O. ; GIPSA-Lab., CNRS, St. Martin d''Heres, France ; Bombrun, L. ; Vasile, G. ; Ferro-Famil, L.
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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 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.

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Selected Topics in Signal Processing, IEEE Journal of  (Volume:5 ,  Issue: 3 )