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2-D Rayleigh autoregressive moving average model for SAR image modeling

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  • Palm, Bruna G.
  • Bayer, Fábio M.
  • Cintra, Renato J.

Abstract

Two-dimensional (2-D) autoregressive moving average (ARMA) models are commonly applied to describe real-world image data, usually assuming Gaussian or symmetric noise. However, real-world data often present non-Gaussian signals, with asymmetrical distributions and strictly positive values. In particular, SAR images are known to be well characterized by the Rayleigh distribution. In this context, the ARMA model tailored for 2-D Rayleigh-distributed data is introduced—the 2-D RARMA model. The 2-D RARMA model is derived and conditional likelihood inferences are discussed. The proposed model was submitted to extensive Monte Carlo simulations to evaluate the performance of the conditional maximum likelihood estimators. Moreover, in the context of SAR image processing, two comprehensive numerical experiments were performed comparing anomaly detection and image modeling results of the proposed model with traditional 2-D ARMA models and competing methods in the literature.

Suggested Citation

  • Palm, Bruna G. & Bayer, Fábio M. & Cintra, Renato J., 2022. "2-D Rayleigh autoregressive moving average model for SAR image modeling," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000330
    DOI: 10.1016/j.csda.2022.107453
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    References listed on IDEAS

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    1. Benjamin M.A. & Rigby R.A. & Stasinopoulos D.M., 2003. "Generalized Autoregressive Moving Average Models," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 214-223, January.
    2. Ella R Rothermel & Matthew T Balazik & Jessica E Best & Matthew W Breece & Dewayne A Fox & Benjamin I Gahagan & Danielle E Haulsee & Amanda L Higgs & Michael H P O’Brien & Matthew J Oliver & Ian A Par, 2020. "Comparative migration ecology of striped bass and Atlantic sturgeon in the US Southern mid-Atlantic bight flyway," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-24, June.
    3. Andréa Rocha & Francisco Cribari-Neto, 2009. "Beta autoregressive moving average models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 529-545, November.
    4. Vinícius T. Scher & Francisco Cribari‐Neto & Guilherme Pumi & Fábio M. Bayer, 2020. "Goodness‐of‐fit tests for βARMA hydrological time series modeling," Environmetrics, John Wiley & Sons, Ltd., vol. 31(3), May.
    5. Ojeda, Silvia & Vallejos, Ronny & Bustos, Oscar, 2010. "A new image segmentation algorithm with applications to image inpainting," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2082-2093, September.
    6. Melchior, Cristiane & Zanini, Roselaine Ruviaro & Guerra, Renata Rojas & Rockenbach, Dinei A., 2021. "Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches," International Journal of Forecasting, Elsevier, vol. 37(2), pages 825-837.
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