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

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

Computational Statistics & Data Analysis, 2022, vol. 171, issue C

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.

Keywords: Anomaly detection; ARMA modeling; Rayleigh distribution; SAR images; Two-dimensional models (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000330

DOI: 10.1016/j.csda.2022.107453

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