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Comparing samples from the $${\mathcal {G}}^0$$G0 distribution using a geodesic distance

Author

Listed:
  • Alejandro C. Frery

    (Universidade Federal de Alagoas)

  • Juliana Gambini

    (Instituto Tecnológico de Buenos Aires
    Universidad Nacional de Tres de Febrero)

Abstract

The $${\mathcal {G}}^0$$G0 distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degrees of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for comparing samples from the $${\mathcal {G}}^0$$G0 distribution using a geodesic distance (GD) as a measure of dissimilarity between models. The objective is quantifying the difference between pairs of samples from SAR data using both local parameters (scale and texture) of the $${\mathcal {G}}^0$$G0 distribution. We propose three tests based on the GD which combine the tests presented in Naranjo-Torres et al. (IEEE J Sel Top Appl Earth Obs Remote Sens 10(3):987–997, 2017), and we estimate their probability distributions using permutation methods.

Suggested Citation

  • Alejandro C. Frery & Juliana Gambini, 2020. "Comparing samples from the $${\mathcal {G}}^0$$G0 distribution using a geodesic distance," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 359-378, June.
    • Handle: RePEc:spr:testjl:v:29:y:2020:i:2:d:10.1007_s11749-019-00658-2
    DOI: 10.1007/s11749-019-00658-2
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    References listed on IDEAS

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    1. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
    2. Salicru, M. & Morales, D. & Menendez, M. L. & Pardo, L., 1994. "On the Applications of Divergence Type Measures in Testing Statistical Hypotheses," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 372-391, November.
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