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Proceedings Paper

GA0 distribution model in edge-preserving parameter estimation of SAR images
Author(s): Julio Cesar Jacobo Berlles; Marta Mejail; Alejandro C. Frery
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Paper Abstract

The multiplicative model has been widely used to explain the statistical properties of SAR images. In it, the model for the image Z is a 2D random field, that is regarded as the result of the product of X, the backscatter that depends on the physical characteristics of the sensed area, and Y, the speckle that depends on the number of looks used to generate the image Z. The most famous distribution for SAR images based on the multiplicative model is the K distribution (Jackeman et al). Recently Frery et al. proposed an alternative distribution, the G0A((alpha) ,(gamma) ,n) distribution which models very well extremely heterogenous areas (cities) as well as moderately heterogeneous areas (forest) and homogeneous areas (crop fields). The ground truth at each pixel can be characterized by the statistical parameters (alpha) and (gamma) , while n is constant for all of the pixels. The purpose of estimating these parameters for every pixel is twofold: first, it can be used to perform a segmentation process and, second, it can be used for gray level restoration. In this work we follow a Markov random field approach and propose an energy function derived from the statistical model adopted: G0A((alpha) ,(gamma) ,n). Edge- preservation is taken into account implicitly in the energy function.

Paper Details

Date Published: 24 September 1998
PDF: 7 pages
Proc. SPIE 3457, Mathematical Modeling and Estimation Techniques in Computer Vision, (24 September 1998); doi: 10.1117/12.323446
Show Author Affiliations
Julio Cesar Jacobo Berlles, Univ. de Buenos Aires (Argentina)
Marta Mejail, Univ. de Buenos Aires (Argentina)
Alejandro C. Frery, Univ. Federal de Pernambuco (Brazil)


Published in SPIE Proceedings Vol. 3457:
Mathematical Modeling and Estimation Techniques in Computer Vision
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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