The GA0 distribution model in edge-preserving parameter estimation of SAR images

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 2-D 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 s...

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Detalles Bibliográficos
Publicado: 1998
Materias:
Backscattering
Computer simulation
Image reconstruction
Image segmentation
Markov processes
Mathematical models
Parameter estimation
Probability density function
Probability distributions
Speckle
Statistical methods
Synthetic aperture radar
Edge preserving parameter estimation
Energy function
Gray level restoration
Ground truth
Markov random field
Multiplicative model
Image analysis
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v3457_n_p209_JacoboBerlles
http://hdl.handle.net/20.500.12110/paper_0277786X_v3457_n_p209_JacoboBerlles
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_0277786X_v3457_n_p209_JacoboBerlles
record_format dspace
spelling paper:paper_0277786X_v3457_n_p209_JacoboBerlles2023-06-08T15:26:10Z The GA0 distribution model in edge-preserving parameter estimation of SAR images Backscattering Computer simulation Image reconstruction Image segmentation Markov processes Mathematical models Parameter estimation Probability density function Probability distributions Speckle Statistical methods Synthetic aperture radar Edge preserving parameter estimation Energy function Gray level restoration Ground truth Markov random field Multiplicative model Image analysis 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 2-D 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 GA 0 (α, γ, n) distribution which models very well extremely heterogeneous 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 α and γ, 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: GA 0 (α, γ, n). Egde-preservation is taken into account implicitly in the energy function. 1998 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v3457_n_p209_JacoboBerlles http://hdl.handle.net/20.500.12110/paper_0277786X_v3457_n_p209_JacoboBerlles
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Backscattering
Computer simulation
Image reconstruction
Image segmentation
Markov processes
Mathematical models
Parameter estimation
Probability density function
Probability distributions
Speckle
Statistical methods
Synthetic aperture radar
Edge preserving parameter estimation
Energy function
Gray level restoration
Ground truth
Markov random field
Multiplicative model
Image analysis
spellingShingle Backscattering
Computer simulation
Image reconstruction
Image segmentation
Markov processes
Mathematical models
Parameter estimation
Probability density function
Probability distributions
Speckle
Statistical methods
Synthetic aperture radar
Edge preserving parameter estimation
Energy function
Gray level restoration
Ground truth
Markov random field
Multiplicative model
Image analysis
The GA0 distribution model in edge-preserving parameter estimation of SAR images
topic_facet Backscattering
Computer simulation
Image reconstruction
Image segmentation
Markov processes
Mathematical models
Parameter estimation
Probability density function
Probability distributions
Speckle
Statistical methods
Synthetic aperture radar
Edge preserving parameter estimation
Energy function
Gray level restoration
Ground truth
Markov random field
Multiplicative model
Image analysis
description 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 2-D 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 GA 0 (α, γ, n) distribution which models very well extremely heterogeneous 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 α and γ, 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: GA 0 (α, γ, n). Egde-preservation is taken into account implicitly in the energy function.
title The GA0 distribution model in edge-preserving parameter estimation of SAR images
title_short The GA0 distribution model in edge-preserving parameter estimation of SAR images
title_full The GA0 distribution model in edge-preserving parameter estimation of SAR images
title_fullStr The GA0 distribution model in edge-preserving parameter estimation of SAR images
title_full_unstemmed The GA0 distribution model in edge-preserving parameter estimation of SAR images
title_sort ga0 distribution model in edge-preserving parameter estimation of sar images
publishDate 1998
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v3457_n_p209_JacoboBerlles
http://hdl.handle.net/20.500.12110/paper_0277786X_v3457_n_p209_JacoboBerlles
_version_ 1768544317673046016

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