Low-cost robust estimation for the single-look I0 model using the Pareto distribution

"The statistical properties of Synthetic Aperture Radar (SAR) image texture reveal useful target characteristics. It is well-known that these images are affected by speckle and prone to extreme values due to double bounce and corner reflectors. The G0 I distribution is flexible enough to model...

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Autores principales: Chan, Debora, Rey, Andrea, Gambini, Juliana, Frery, Alejandro C.
Formato: Artículos de Publicaciones Periódicas publishedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
DISTRIBUCION
ESTIMACION DE PARAMETROS
RADAR DE APERTURA SINTETICA
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/3259
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-3259
record_format dspace
spelling I32-R138-123456789-32592022-12-07T13:06:16Z Low-cost robust estimation for the single-look I0 model using the Pareto distribution Chan, Debora Rey, Andrea Gambini, Juliana Frery, Alejandro C. DISTRIBUCION ESTIMACION DE PARAMETROS RADAR DE APERTURA SINTETICA "The statistical properties of Synthetic Aperture Radar (SAR) image texture reveal useful target characteristics. It is well-known that these images are affected by speckle and prone to extreme values due to double bounce and corner reflectors. The G0 I distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: α, related to the texture, γ , a scale parameter, and L, the number of looks. Quality estimation of α is essential due to its immediate interpretability. In this letter, we exploit the connection between the G0 I and Pareto distributions. With this, we obtain six estimators that have not been previously used in the SAR literature. We compare their behavior with others in the noisiest case for monopolarized intensity data, namely single look case. We evaluate them using Monte Carlo methods for noncontaminated and contaminated data, considering convergence rate, bias, mean squared error, and computational time. We conclude that two of these estimators based on the Pareto law are the safest choices when dealing with actual data and small samples, as is the case of despeckling techniques and segmentation, to name just two applications. We verify the results with an actual SAR image." 2020-12-17T15:33:30Z 2020-12-17T15:33:30Z 2020 Artículos de Publicaciones Periódicas info:eu-repo/semantics/publishedVersion http://ri.itba.edu.ar/handle/123456789/3259 en http://creativecommons.org/licenses/by-nc-sa/X.0/ application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic DISTRIBUCION
ESTIMACION DE PARAMETROS
RADAR DE APERTURA SINTETICA
spellingShingle DISTRIBUCION
ESTIMACION DE PARAMETROS
RADAR DE APERTURA SINTETICA
Chan, Debora
Rey, Andrea
Gambini, Juliana
Frery, Alejandro C.
Low-cost robust estimation for the single-look I0 model using the Pareto distribution
topic_facet DISTRIBUCION
ESTIMACION DE PARAMETROS
RADAR DE APERTURA SINTETICA
description "The statistical properties of Synthetic Aperture Radar (SAR) image texture reveal useful target characteristics. It is well-known that these images are affected by speckle and prone to extreme values due to double bounce and corner reflectors. The G0 I distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: α, related to the texture, γ , a scale parameter, and L, the number of looks. Quality estimation of α is essential due to its immediate interpretability. In this letter, we exploit the connection between the G0 I and Pareto distributions. With this, we obtain six estimators that have not been previously used in the SAR literature. We compare their behavior with others in the noisiest case for monopolarized intensity data, namely single look case. We evaluate them using Monte Carlo methods for noncontaminated and contaminated data, considering convergence rate, bias, mean squared error, and computational time. We conclude that two of these estimators based on the Pareto law are the safest choices when dealing with actual data and small samples, as is the case of despeckling techniques and segmentation, to name just two applications. We verify the results with an actual SAR image."
format Artículos de Publicaciones Periódicas
publishedVersion
author Chan, Debora
Rey, Andrea
Gambini, Juliana
Frery, Alejandro C.
author_facet Chan, Debora
Rey, Andrea
Gambini, Juliana
Frery, Alejandro C.
author_sort Chan, Debora
title Low-cost robust estimation for the single-look I0 model using the Pareto distribution
title_short Low-cost robust estimation for the single-look I0 model using the Pareto distribution
title_full Low-cost robust estimation for the single-look I0 model using the Pareto distribution
title_fullStr Low-cost robust estimation for the single-look I0 model using the Pareto distribution
title_full_unstemmed Low-cost robust estimation for the single-look I0 model using the Pareto distribution
title_sort low-cost robust estimation for the single-look i0 model using the pareto distribution
publishDate 2020
url http://ri.itba.edu.ar/handle/123456789/3259
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AT reyandrea lowcostrobustestimationforthesinglelooki0modelusingtheparetodistribution
AT gambinijuliana lowcostrobustestimationforthesinglelooki0modelusingtheparetodistribution
AT freryalejandroc lowcostrobustestimationforthesinglelooki0modelusingtheparetodistribution
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