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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2015
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19391404_v8_n1_p365_Gambini http://hdl.handle.net/20.500.12110/paper_19391404_v8_n1_p365_Gambini |
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paper:paper_19391404_v8_n1_p365_Gambini |
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paper:paper_19391404_v8_n1_p365_Gambini2023-06-08T16:32:14Z Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels 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. 2015 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19391404_v8_n1_p365_Gambini 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 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. |
| 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 |
| publishDate |
2015 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19391404_v8_n1_p365_Gambini http://hdl.handle.net/20.500.12110/paper_19391404_v8_n1_p365_Gambini |
| _version_ |
1768544104304607232 |