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Gambini, J.; Cassetti, J.; Lucini, M.M.; Frery, A.C. "Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels" (2015) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(1):365-375
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Abstract:

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.

Registro:

Documento: Artículo
Título:Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
Autor:Gambini, J.; Cassetti, J.; Lucini, M.M.; Frery, A.C.
Filiación:Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
Depto. de Ingenieria en Computacion, Universidad Nacional de Tres de Febrero, Pcia. de Buenos Aires, Buenos Aires, Argentina
Instituto de Desarrollo Humano, Universidad Nacional de Gral. Sarmiento, Buenos Aires, Argentina
Facultad de Ciencias Exactas, Naturales y Agrimensura, Universidad Nacional Del Nordeste, Corrientes, Argentina
LaCCAN, Universidade Federal de Alagoas, Maceio, Brazil
Palabras clave: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
Año:2015
Volumen:8
Número:1
Página de inicio:365
Página de fin:375
DOI: http://dx.doi.org/10.1109/JSTARS.2014.2346017
Título revista:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Título revista abreviado:IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
ISSN:19391404
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19391404_v8_n1_p365_Gambini

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Citas:

---------- APA ----------
Gambini, J., Cassetti, J., Lucini, M.M. & Frery, A.C. (2015) . Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 365-375.
http://dx.doi.org/10.1109/JSTARS.2014.2346017
---------- CHICAGO ----------
Gambini, J., Cassetti, J., Lucini, M.M., Frery, A.C. "Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels" . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 1 (2015) : 365-375.
http://dx.doi.org/10.1109/JSTARS.2014.2346017
---------- MLA ----------
Gambini, J., Cassetti, J., Lucini, M.M., Frery, A.C. "Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels" . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 1, 2015, pp. 365-375.
http://dx.doi.org/10.1109/JSTARS.2014.2346017
---------- VANCOUVER ----------
Gambini, J., Cassetti, J., Lucini, M.M., Frery, A.C. Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015;8(1):365-375.
http://dx.doi.org/10.1109/JSTARS.2014.2346017