Sampling from the 𝒢I distribution

Chan Debora (), Rey Andrea (), Gambini Juliana () and Frery Alejandro C. ()
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Chan Debora: Centro de Procesamiento de Señales e Imágenes (CPSI), Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Medrano 951, Ciudad Autónoma de Buenos Aires, Argentina
Rey Andrea: Centro de Procesamiento de Señales e Imágenes (CPSI), Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Medrano 951, Ciudad Autónoma de Buenos Aires, Argentina
Gambini Juliana: Departamento de Ingeniería Informática, ITBA & Departamento de Ingeniería en Computación, UNTreF, Instituto Tecnológico de Buenos Aires & Universidad Nacional de Tres de Febrero, Lavardén 315, Ciudad Autónoma de Buenos Aires, Argentina
Frery Alejandro C.: Laboratório de Computação Científica e Análise Numérica – LaCCAN, Universidade Federal de Alagoas, Av. Lourival Melo Mota, s/n, Tabuleiro do Martins, Maceió, Brazil

Monte Carlo Methods and Applications, 2018, vol. 24, issue 4, 271-287

Abstract: Synthetic Aperture Radar (SAR) images are widely used in several environmental applications because they provide information which cannot be obtained with other sensors. The 𝒢I0{\mathcal{G}_{I}^{0}} distribution is an important model for these images because of its flexibility (it provides a suitable way for modeling areas with different degrees of texture, reflectivity and signal-to-noise ratio) and tractability (it is closely related to the Snedekor-F, Pareto Type II, and Gamma distributions). Simulated data are important for devising tools for SAR image processing, analysis and interpretation, among other applications. We compare four ways for sampling data that follow the 𝒢I0{\mathcal{G}_{I}^{0}} distribution, using several criteria for assessing the quality of the generated data and the consumed processing time. The experiments are performed running codes in four different programming languages. The experimental results indicate that although there is no overall best method in all the considered programming languages, it is possible to make specific recommendations for each one.

Keywords: Random variable generation; SAR image modeling; programming languages; accuracy and goodness of fit (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/mcma-2018-2023

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