Displacement Estimation by Maximum-Likelihood Texture Tracking

Author

Harant, Olivier ; Bombrun, Lionel ; Vasile, Gabriel ; Ferro-Famil, Laurent ; Gay, Michel

Author_Institution

GIPSA-Lab., CNRS, St. Martin d´´Heres, France

Volume

5

Issue

3

fYear

2011

fDate

6/1/2011 12:00:00 AM

Firstpage

398

Lastpage

407

Abstract

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.

Keywords

geophysical image processing; image texture; maximum likelihood estimation; radar polarimetry; radar tracking; remote sensing by radar; speckle; synthetic aperture radar; Fisher probability density function modeling; RADARSAT-2 sensor; TerraSAR-X sensor; displacement estimation; maximum-likelihood texture tracking; polarimetric synthetic aperture radar data-set; random variables; ratio distribution; scalar texture parameter; simulated data; spaceborne images; speckle component; spherically invariant random vectors theory; Adaptation model; Coherence; Covariance matrix; Data models; Maximum likelihood estimation; Pixel; Radar tracking; Maximum-likelihood (ML); offset tracking; polarimetric synthetic aperture radar (SAR); spherically invariant random vectors; texture;

fLanguage

English

Journal_Title

Selected Topics in Signal Processing, IEEE Journal of

Publisher

ieee

ISSN

1932-4553

Type

jour

DOI

10.1109/JSTSP.2010.2100365

Filename

5671450