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Sampling from the đť’˘I0 distribution

  • Debora Chan ORCID logo EMAIL logo , Andrea Rey , Juliana Gambini ORCID logo und Alejandro C. Frery ORCID logo
Veröffentlicht/Copyright: 16. Oktober 2018

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 đť’˘I0 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 đť’˘I0 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.

MSC 2010: 90-04

References

[1] H. Allende, A. C. Frery, J. Galbiati and L. Pizarro, M-Estimators with asymmetric influence functions: The 𝒢A0 distribution case, J. Stat. Comput. Simul. 76 (2006), no. 11, 941–956. 10.1080/10629360600569154Suche in Google Scholar

[2] S. Auer, M. Horning, I. Schmitt and P. Reinartz, Simulation-based interpretation and alignment of high-resolution optical and SAR images, IEEE J. Appl. Earth Observ. Remote Sensing 10 (2017), 4779–4793. 10.1109/JSTARS.2017.2723082Suche in Google Scholar

[3] M. Barber, F. Grings, P. Perna, M. Piscitelli, M. Maas, C. Bruscantini, J. Jacobo-Berlles and H. Karszenbaum, Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data, IEEE J. Appl. Earth Observ. Remote Sensing 5 (2012), no. 3, 942–951. 10.1109/JSTARS.2012.2191266Suche in Google Scholar

[4] J. Bezanson, A. Edelman, S. Karpinski and V. Shah, Julia: A Fresh Approach to Numerical Computing, Cornell University Library, New York, 2014. Suche in Google Scholar

[5] S. Canale, A. De Santis, D. Iacoviello, F. Pirri and S. Sagratella, Integrating X-SAR images and anthropic factors for fire susceptibility assessment, IEEE International Geoscience and Remote Sensing Symposium, IEEE Press, Piscataway (2011), 818–821. 10.1109/IGARSS.2011.6049256Suche in Google Scholar

[6] S. Chen, Y. Li and X. Wang, Crop discrimination based on polarimetric correlation coefficients optimization for PolSAR data, Int. J. Remote Sensing 36 (2015), no. 16, 4233–4249. 10.1080/01431161.2015.1079345Suche in Google Scholar

[7] S. W. Chen and M. Sato, Tsunami damage investigation of built-up areas using multitemporal spaceborne full polarimetric SAR images, IEEE Trans. Geosci. Remote Sensing 51 (2013), no. 4, 1985–1997. 10.1109/TGRS.2012.2210050Suche in Google Scholar

[8] D. Cozzolino, S. Parrilli, G. Scarpa, G. Poggi and L. Verdoliva, Fast adaptive nonlocal SAR despeckling, IEEE Trans. Geosci. Remote Sensing Lett. 11 (2014), 524–528. 10.1109/LGRS.2013.2271650Suche in Google Scholar

[9] F. Dell’Acqua and P. Gamba, Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives, Proc. IEEE 100 (2012), no. 10, 2876–2890. 10.1109/JPROC.2012.2196404Suche in Google Scholar

[10] L. Devroye, Non-Uniform Random Variate Generation, Springer, New York, 1986. 10.1007/978-1-4613-8643-8Suche in Google Scholar

[11] J. A. Doornik and M. Ooms, Introduction to Ox, Timberlake Consultants Press, London, 2006. Suche in Google Scholar

[12] E. M. El-Desouki, K. F. A. Hussien and H. M. El-Hennawy, Electromagnetic simulation for land imaging using fully polarimetric SAR system, 34th National Radio Science Conference, IEEE Press, Piscataway (2017), 132–141. 10.1109/NRSC.2017.7893470Suche in Google Scholar

[13] T. Esch, M. Schmidt, M. Breunig, A. Felbier, H. Taubenböck, W. Heldens, C. Riegler, A. Roth and S. Dech, Identification and characterization of urban structures using VHR SAR data, IEEE International on Geoscience and Remote Sensing Symposium (IGARSS), IEEE Press, Piscataway (2011), 1413–1416. 10.1109/IGARSS.2011.6049331Suche in Google Scholar

[14] A. Frery, H. Müller, C. Yanasse and S. Sant’Anna, A model for extremely heterogeneous clutter, IEEE Trans. Geosci. Remote Sensing 35 (1997), no. 3, 648–659. 10.1109/36.581981Suche in Google Scholar

[15] M. Friedman, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J. Amer. Statist. Assoc. 32 (1937), no. 200, 675–701. 10.1080/01621459.1937.10503522Suche in Google Scholar

[16] J. Gambini, J. Cassetti, M. Lucini and A. Frery, Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sensing 8 (2015), no. 1, 365–375. 10.1109/JSTARS.2014.2346017Suche in Google Scholar

[17] J. Gambini, M. Mejail, J. Jacobo-Berlles and A. Frery, Accuracy of local edge detection in speckled imagery, Stat. Comput. 18 (2008), no. 1, 15–26. 10.1007/s11222-007-9034-ySuche in Google Scholar

[18] G. Gao, Statistical modeling of SAR images: A survey, Sensors 10 (2010), no. 1, 775–795. 10.3390/s100100775Suche in Google Scholar PubMed PubMed Central

[19] E. Girón, A. C. Frery and F. Cribari-Neto, Nonparametric edge detection in speckled imagery, Math. Comput. Simulation 82 (2012), no. 11, 2182–2198. 10.1016/j.matcom.2012.04.013Suche in Google Scholar

[20] L. Gomez, R. Ospina and A. C. Frery, Unassisted quantitative evaluation of despeckling filters, Remote Sensing 9 (2017), 10.3390/rs9040389. 10.3390/rs9040389Suche in Google Scholar

[21] P. Guccione, L. Mascolo, G. Nico, A. Pelusi and M. Zonno, SAR image simulation of ocean environment and detection of oil slicks, 10th European Conference on Synthetic Aperture Radar, IEEE Press, Piscataway (2014), 1–4. Suche in Google Scholar

[22] M. Kiemer and H. Breit, Eficient evaluation of multichanel SAR data recombination filters, IEEE Trans. Geosci. Remote Sensing 55 (2017), 6277–6286. 10.1109/TGRS.2017.2724918Suche in Google Scholar

[23] W. H. Kruskal and W. A. Wallis, Use of ranks in one-criterion variance analysis, J. Amer. Statist. Assoc. 47 (1952), no. 260, 583–621. 10.1080/01621459.1952.10483441Suche in Google Scholar

[24] F. Massey, Jr., The Kolmogorov–Smirnov test for goodness of fit, J. Amer. Statist. Assoc. 46 (1951), no. 253, 68–78. 10.1080/01621459.1951.10500769Suche in Google Scholar

[25] M. Mejail, J. C. Jacobo-Berlles, A. C. Frery and O. H. Bustos, Classification of SAR images using a general and tractable multiplicative model, Int. J. Remote Sensing 24 (2003), no. 18, 3565–3582. 10.1080/0143116021000053274Suche in Google Scholar

[26] R. T. Melrose, R. T. Kingsford and A. K. Milne, Using radar to detect flooding in arid wetlands and rivers, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE Press, Piscataway (2012), 5242–524. 10.1109/IGARSS.2012.6352427Suche in Google Scholar

[27] E. Moschetti, M. G. Palacio, M. Picco, O. H. Bustos and A. C. Frery, On the use of Lee’s protocol for speckle-reducing techniques, Latin Amer. Appl. Res. 36 (2006), no. 2, 115–121. Suche in Google Scholar

[28] J. Naranjo-Torres, J. Gambini and A. C. Frery, The geodesic distance between 𝒢I0 models and its application to region discrimination, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sensing 10 (2017), no. 3, 987–997. 10.1109/JSTARS.2017.2647846Suche in Google Scholar

[29] A. D. C. Nascimento, R. J. Cintra and A. C. Frery, Hypothesis testing in speckled data with stochastic distances, IEEE Trans. Geosci. Remote Sensing 48 (2010), no. 1, 373–385. 10.1109/TGRS.2009.2025498Suche in Google Scholar

[30] A. D. C. Nascimento, M. M. Horta, A. C. Frery and R. J. Cintra, Comparing edge detection methods based on stochastic entropies and distances for PolSAR imagery, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sensing 7 (2014), no. 2, 648–663. 10.1109/JSTARS.2013.2266319Suche in Google Scholar

[31] M. Newman, Power laws, Pareto distributions and Zipf’s law, Contemp. Phys. 46 (2005), 323–351. 10.1080/00107510500052444Suche in Google Scholar

[32] C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images, Artech House, Boston, 1998. Suche in Google Scholar

[33] N. Razali and Y. Wah, Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests, J. Stat. Model. Anal. 2 (2011), no. 1, 21–33. Suche in Google Scholar

[34] J. Rojo, Heavy tailed densities, Wiley Interdiscip. Rev. Comput. Statist. 5 (2013), no. 1, 30–40. 10.1002/wics.1236Suche in Google Scholar

[35] R. Roy, Comparison of different techniques to generate normal random variables, J. East Central Europe 545 (2002), 5–6. Suche in Google Scholar

[36] S. J. S. Sant’Anna, J. C. S. Lacava and D. Fernandes, From Maxwell’s equations to polarimetric SAR images: A simulation approach, Sensors 8 (2008), 7380–7409. 10.3390/s8117380Suche in Google Scholar PubMed PubMed Central

[37] M. Sato, S. W. Chen and M. Satake, Polarimetric SAR analysis of tsunami damage following the March 11, 2011 East Japan earthquake, Proc. IEEE 100 (2012), no. 10, 2861–2875. 10.1109/JPROC.2012.2200649Suche in Google Scholar

[38] W. B. Silva, C. C. Freitas, S. J. S. Sant’Anna and A. C. Frery, Classification of segments in PolSAR imagery by minimum stochastic distances between Wishart distributions, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sensing 6 (2013), no. 3, 1263–1273. 10.1109/JSTARS.2013.2248132Suche in Google Scholar

[39] C. D. Storie, J. Storie and G. Salinas de Salmuni, Urban boundary extraction using 2-component polarimetric SAR decomposition, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE Press, Piscataway (2012), 5741–5744. 10.1109/IGARSS.2012.6352307Suche in Google Scholar

[40] L. Torres, S. J. S. Sant’Anna, C. C. Freitas and A. C. Frery, Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means, Pattern Recognit. 47 (2014), 141–157. 10.1016/j.patcog.2013.04.001Suche in Google Scholar

[41] D. Velotto, S. Lehner, A. Soloviev and C. Maingot, Analysis of oceanic features from dual-polarization high resolution X-band SAR imagery for oil spill detection purposes, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE Press, Piscataway (2012), 2841–2844. 10.1109/IGARSS.2012.6350840Suche in Google Scholar

[42] J. A. Villaseñor-Alva and E. González-Estrada, A bootstrap goodness of fit test for the generalized Pareto distribution, Comput. Statist. Data Anal. 53 (2009), no. 11, 3835–3841. 10.1016/j.csda.2009.04.001Suche in Google Scholar

[43] C. Wang, X. Wen and H. Xu, A robust estimator of parameters for 𝒢I0-modeled SAR imagery based on random weighting method, EURASIP J. Adv. Signal Process. 22 (2017), 42–52. 10.1186/s13634-017-0452-5Suche in Google Scholar

[44] Q. Wu, R. Chen, H. Sun and Y. Cao, Urban building density detection using high resolution SAR imagery, Joint Urban Remote Sensing Event, IEEE Press, Piscataway (2011), 45–48. 10.1109/JURSE.2011.5764715Suche in Google Scholar

[45] Y. Yamaguchi, Disaster monitoring by fully Polarimetric SAR data acquired with ALOS-PALSAR, Proc. IEEE 100 (2012), no. 10, 2851–2860. 10.1109/JPROC.2012.2195469Suche in Google Scholar

[46] MathWorks, MatLab, The language of technical computing, The MathWorks, 2019. Suche in Google Scholar

[47] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2016. Suche in Google Scholar

Received: 2018-06-12
Revised: 2018-09-25
Accepted: 2018-09-27
Published Online: 2018-10-16
Published in Print: 2018-12-01

© 2018 Walter de Gruyter GmbH, Berlin/Boston

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