[Bea2004a]
Segmentation of Textured Polarimetric SAR Scenes by Likelihood Approximation

Authors:Beaulieu Jean-Marie, Ridha Touzi

Journal:IEEE Transactions on Geoscience and Remote Sensing

 Oct., 2004, vol. 42, issue 10, pp. 2063-2072

ISSN:0196-2892

URL:https://ieeexplore.ieee.org/document/1344159

DOI:10.1109/TGRS.2004.835302

Abstract:   A hierarchical stepwise optimization process is developed for polarimetric synthetic aperture radar image segmentation. We show that image segmentation can be viewed as a likelihood approximation problem. The likelihood segment merging criteria are derived using the multivariate complex Gaussian, the Wishart distribution, and the K-distribution. In the presence of spatial texture, the Gaussian-Wishart segmentation is not appropriate. The K-distribution segmentation is more effective in textured forested areas. The validity of the product model is also assessed, and a field-adaptable segmentation strategy combining different criteria is examined.

@Article{Bea2004a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
title = {Segmentation of Textured Polarimetric {SAR} Scenes by Likelihood Approximation},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {42},
number = {10},
pages = {2063-2072},
year = {2004},
month = {Oct.},
abstract = {A hierarchical stepwise optimization process is developed for polarimetric synthetic aperture radar image segmentation. We show that image segmentation can be viewed as a likelihood approximation problem. The likelihood segment merging criteria are derived using the multivariate complex Gaussian, the Wishart distribution, and the K-distribution. In the presence of spatial texture, the Gaussian-Wishart segmentation is not appropriate. The K-distribution segmentation is more effective in textured forested areas. The validity of the product model is also assessed, and a field-adaptable segmentation strategy combining different criteria is examined.},
publisher = {},
url = {https://ieeexplore.ieee.org/document/1344159},
issn = {0196-2892},
doi = {10.1109/TGRS.2004.835302},
mypdf = {11},
address = {},
keywords = {}
}

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