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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Institute of Electrical and Electronics Engineers
2015
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| LEADER | 11017caa a22011417a 4500 | ||
|---|---|---|---|
| 001 | PAPER-16770 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518204748.0 | ||
| 008 | 180614s2015 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-85027920014 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Gambini, J. | |
| 245 | 1 | 0 | |a Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels |
| 260 | |b Institute of Electrical and Electronics Engineers |c 2015 | ||
| 506 | |2 openaire |e Política editorial | ||
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| 520 | 3 | |a 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. |l eng | |
| 593 | |a Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina | ||
| 593 | |a Depto. de Ingenieria en Computacion, Universidad Nacional de Tres de Febrero, Pcia. de Buenos Aires, Buenos Aires, Argentina | ||
| 593 | |a Instituto de Desarrollo Humano, Universidad Nacional de Gral. Sarmiento, Buenos Aires, Argentina | ||
| 593 | |a Facultad de Ciencias Exactas, Naturales y Agrimensura, Universidad Nacional Del Nordeste, Corrientes, Argentina | ||
| 593 | |a LaCCAN, Universidade Federal de Alagoas, Maceio, Brazil | ||
| 690 | 1 | 0 | |a FEATURE EXTRACTION |
| 690 | 1 | 0 | |a IMAGE TEXTURE ANALYSIS |
| 690 | 1 | 0 | |a SPECKLE |
| 690 | 1 | 0 | |a STATISTICS |
| 690 | 1 | 0 | |a SYNTHETIC APERTURE RADAR (SAR) |
| 690 | 1 | 0 | |a FEATURE EXTRACTION |
| 690 | 1 | 0 | |a IMAGE TEXTURE |
| 690 | 1 | 0 | |a RADAR IMAGING |
| 690 | 1 | 0 | |a SPECKLE |
| 690 | 1 | 0 | |a STATISTICS |
| 690 | 1 | 0 | |a STOCHASTIC SYSTEMS |
| 690 | 1 | 0 | |a SYNTHETIC APERTURE RADAR |
| 690 | 1 | 0 | |a TEXTURES |
| 690 | 1 | 0 | |a IMAGE TEXTURE ANALYSIS |
| 690 | 1 | 0 | |a INVERSE GAUSSIAN DENSITY |
| 690 | 1 | 0 | |a NUMERICAL PROBLEMS |
| 690 | 1 | 0 | |a ROUGHNESS PARAMETERS |
| 690 | 1 | 0 | |a SYNTHETIC APERTURE RADAR IMAGERY |
| 690 | 1 | 0 | |a TEXTURE PARAMETERS |
| 690 | 1 | 0 | |a THEORETICAL DENSITY |
| 690 | 1 | 0 | |a THREE PARAMETERS |
| 690 | 1 | 0 | |a PARAMETER ESTIMATION |
| 690 | 1 | 0 | |a IMAGE ANALYSIS |
| 690 | 1 | 0 | |a IMAGERY |
| 690 | 1 | 0 | |a ROUGHNESS |
| 690 | 1 | 0 | |a SPECKLE |
| 690 | 1 | 0 | |a STOCHASTICITY |
| 690 | 1 | 0 | |a SYNTHETIC APERTURE RADAR |
| 650 | 1 | 7 | |2 spines |a RADAR |
| 700 | 1 | |a Cassetti, J. | |
| 700 | 1 | |a Lucini, M.M. | |
| 700 | 1 | |a Frery, A.C. | |
| 773 | 0 | |d Institute of Electrical and Electronics Engineers, 2015 |g v. 8 |h pp. 365-375 |k n. 1 |x 19391404 |t IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. | |
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| 856 | 4 | 0 | |u https://doi.org/10.1109/JSTARS.2014.2346017 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_19391404_v8_n1_p365_Gambini |y Handle |
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| 961 | |a paper_19391404_v8_n1_p365_Gambini |b paper |c PE | ||
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| 999 | |c 77723 | ||