.png){kind=icon}
.png){kind=icon}
Return
Displacement Estimation by Maximum-Likelihood Texture Tracking
.png){kind=icon}
OA
AI
DOI：10.1109/JSTSP.2010.2100365.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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:
Maximum-likelihood (ML)
offset tracking
polarimetric synthetic aperture radar (SAR)
spherically invariant random vectors
texture
Journal
IF:
13.7
Papers:
1.9K
Citations:
1.1W