Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels

In this paper, we analyze several strategies for the estimation of the roughness parameter of the GI 0 distribution. It has been shown that this distribution is able to characterize a large number of targets in monopolarized synthetic aperture radar (SAR) imagery, deserving the denomination of '...

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Autores principales: Gambini, J., Cassetti, J., Lucini, M.M., Frery, A.C.
Formato: JOUR
Materias:
Feature extraction
image texture analysis
speckle
statistics
Synthetic aperture radar (SAR)
Image texture
Radar
Radar imaging
Speckle
Statistics
Stochastic systems
Synthetic aperture radar
Textures
Image texture analysis
Inverse Gaussian density
Numerical problems
Roughness parameters
Synthetic Aperture Radar Imagery
Texture parameters
Theoretical density
Three parameters
Parameter estimation
image analysis
imagery
roughness
stochasticity
synthetic aperture radar
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_19391404_v8_n1_p365_Gambini
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_19391404_v8_n1_p365_Gambini
record_format dspace
spelling todo:paper_19391404_v8_n1_p365_Gambini2023-10-03T16:36:47Z Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels Gambini, J. Cassetti, J. Lucini, M.M. Frery, A.C. Feature extraction image texture analysis speckle statistics Synthetic aperture radar (SAR) Feature extraction Image texture Radar Radar imaging Speckle Statistics Stochastic systems Synthetic aperture radar Textures Image texture analysis Inverse Gaussian density Numerical problems Roughness parameters Synthetic Aperture Radar Imagery Texture parameters Theoretical density Three parameters Parameter estimation image analysis imagery roughness speckle stochasticity synthetic aperture radar In this paper, we analyze several strategies for the estimation of the roughness parameter of the GI 0 distribution. It has been shown that this distribution is able to characterize a large number of targets in monopolarized synthetic aperture radar (SAR) imagery, deserving the denomination of 'Universal Model.' It is indexed by three parameters: 1) the number of looks (which can be estimated in the whole image); 2) a scale parameter; and 3) the roughness or texture parameter. The latter is closely related to the number of elementary backscatters in each pixel, one of the reasons for receiving attention in the literature. Although there are efforts in providing improved and robust estimates for such quantity, its dependable estimation still poses numerical problems in practice. We discuss estimators based on the minimization of stochastic distances between empirical and theoretical densities and argue in favor of using an estimator based on the triangular distance and asymmetric kernels built with inverse Gaussian densities. We also provide new results regarding the heavy-tailedness of this distribution. © 2008-2012 IEEE. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_19391404_v8_n1_p365_Gambini
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Feature extraction
image texture analysis
speckle
statistics
Synthetic aperture radar (SAR)
Feature extraction
Image texture
Radar
Radar imaging
Speckle
Statistics
Stochastic systems
Synthetic aperture radar
Textures
Image texture analysis
Inverse Gaussian density
Numerical problems
Roughness parameters
Synthetic Aperture Radar Imagery
Texture parameters
Theoretical density
Three parameters
Parameter estimation
image analysis
imagery
roughness
speckle
stochasticity
synthetic aperture radar
spellingShingle Feature extraction
image texture analysis
speckle
statistics
Synthetic aperture radar (SAR)
Feature extraction
Image texture
Radar
Radar imaging
Speckle
Statistics
Stochastic systems
Synthetic aperture radar
Textures
Image texture analysis
Inverse Gaussian density
Numerical problems
Roughness parameters
Synthetic Aperture Radar Imagery
Texture parameters
Theoretical density
Three parameters
Parameter estimation
image analysis
imagery
roughness
speckle
stochasticity
synthetic aperture radar
Gambini, J.
Cassetti, J.
Lucini, M.M.
Frery, A.C.
Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
topic_facet Feature extraction
image texture analysis
speckle
statistics
Synthetic aperture radar (SAR)
Feature extraction
Image texture
Radar
Radar imaging
Speckle
Statistics
Stochastic systems
Synthetic aperture radar
Textures
Image texture analysis
Inverse Gaussian density
Numerical problems
Roughness parameters
Synthetic Aperture Radar Imagery
Texture parameters
Theoretical density
Three parameters
Parameter estimation
image analysis
imagery
roughness
speckle
stochasticity
synthetic aperture radar
description In this paper, we analyze several strategies for the estimation of the roughness parameter of the GI 0 distribution. It has been shown that this distribution is able to characterize a large number of targets in monopolarized synthetic aperture radar (SAR) imagery, deserving the denomination of 'Universal Model.' It is indexed by three parameters: 1) the number of looks (which can be estimated in the whole image); 2) a scale parameter; and 3) the roughness or texture parameter. The latter is closely related to the number of elementary backscatters in each pixel, one of the reasons for receiving attention in the literature. Although there are efforts in providing improved and robust estimates for such quantity, its dependable estimation still poses numerical problems in practice. We discuss estimators based on the minimization of stochastic distances between empirical and theoretical densities and argue in favor of using an estimator based on the triangular distance and asymmetric kernels built with inverse Gaussian densities. We also provide new results regarding the heavy-tailedness of this distribution. © 2008-2012 IEEE.
format JOUR
author Gambini, J.
Cassetti, J.
Lucini, M.M.
Frery, A.C.
author_facet Gambini, J.
Cassetti, J.
Lucini, M.M.
Frery, A.C.
author_sort Gambini, J.
title Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_short Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_full Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_fullStr Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_full_unstemmed Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_sort parameter estimation in sar imagery using stochastic distances and asymmetric kernels
url http://hdl.handle.net/20.500.12110/paper_19391404_v8_n1_p365_Gambini
work_keys_str_mv AT gambinij parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
AT cassettij parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
AT lucinimm parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
AT freryac parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
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