Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels

The Statistical modeling of the data is essential in order to interpret synthetic aperture radar (SAR) images. Speckled data have been described under the multiplicative model using the G family of distributions, which is able to describe rough and extremely rough areas better than the K distribut...

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Autores principales: Gambini, María Juliana, Cassetti, Julia Analía, Lucini, María Magdalena, Frery, Alejandro César
Formato: Artículo
Lenguaje:Inglés
Publicado: Institute of Electrical and Electronics Engineers Inc. 2024
Materias:
Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
Acceso en línea:http://repositorio.unne.edu.ar/handle/123456789/55296
Aporte de:
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste
id I48-R184-123456789-55296
record_format dspace
spelling I48-R184-123456789-552962025-03-06T11:06:34Z Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels Gambini, María Juliana Cassetti, Julia Analía Lucini, María Magdalena Frery, Alejandro César Feature extraction Image texture analysis Speckle Statistics Synthetic apertura radar The Statistical modeling of the data is essential in order to interpret synthetic aperture radar (SAR) images. Speckled data have been described under the multiplicative model using the G family of distributions, which is able to describe rough and extremely rough areas better than the K distribution. The survey article discusses in detail several statistical models for this kind of data. Under the G model, different degrees of roughness are associated with different parameter values; therefore, it is of paramount importance to have high quality estimators. Several works have been devoted to the subject of improving estimation with two main venues of research, namely, analytic and resampling procedures. 2024-09-12T11:00:05Z 2024-09-12T11:00:05Z 2015-01 Artículo Gambini, María Juliana, et al., 2015. Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels. IEEE Journal of selected topics in applied earth observations and remote sensing. New York: Institute of Electrical and Electronics Engineers Inc., vol. 8, no. 1, p. 365-375. ISSN 1939-1404. 1939-1404 http://repositorio.unne.edu.ar/handle/123456789/55296 eng http://dx.doi.org/10.1109/JSTARS.2014.2346017 openAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf p. 365-375 application/pdf Institute of Electrical and Electronics Engineers Inc. IEEE Journal of selected topics in applied earth observations and remote sensing, 2015, vol. 8, no. 1, p. 365-375.
institution Universidad Nacional del Nordeste
institution_str I-48
repository_str R-184
collection RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
language Inglés
topic Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
spellingShingle Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
Gambini, María Juliana
Cassetti, Julia Analía
Lucini, María Magdalena
Frery, Alejandro César
Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
topic_facet Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
description The Statistical modeling of the data is essential in order to interpret synthetic aperture radar (SAR) images. Speckled data have been described under the multiplicative model using the G family of distributions, which is able to describe rough and extremely rough areas better than the K distribution. The survey article discusses in detail several statistical models for this kind of data. Under the G model, different degrees of roughness are associated with different parameter values; therefore, it is of paramount importance to have high quality estimators. Several works have been devoted to the subject of improving estimation with two main venues of research, namely, analytic and resampling procedures.
format Artículo
author Gambini, María Juliana
Cassetti, Julia Analía
Lucini, María Magdalena
Frery, Alejandro César
author_facet Gambini, María Juliana
Cassetti, Julia Analía
Lucini, María Magdalena
Frery, Alejandro César
author_sort Gambini, María Juliana
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
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
url http://repositorio.unne.edu.ar/handle/123456789/55296
work_keys_str_mv AT gambinimariajuliana parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
AT cassettijuliaanalia parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
AT lucinimariamagdalena parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
AT freryalejandrocesar parameterestimationinsarimageryusingstochasticdistancesandasymmetrickernels
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