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

• 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. © 2022 The Authors

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Anomaly detection

ARMA modeling

Rayleigh distribution

SAR images

Two-dimensional models

Gaussian distribution

Gaussian noise (electronic)

Intelligent systems

Maximum likelihood estimation

Monte Carlo methods

Numerical methods

Synthetic aperture radar

Autoregressive Moving Average modeling

Gaussians

Image modeling

Rayleigh

Rayleigh distributions

Real-world image data

Two Dimensional (2 D)

Two dimensional model

Radar imaging

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