2-D Rayleigh Autoregressive Moving Average Model for SAR Image Modeling

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.

arXiv comment21 pages, 8 figures, 6 tables

References in corpus (6)

• An Iterative Wavelet Threshold for Signal Denoising
• Kumaraswamy autoregressive moving average models for double bounded environmental data
• Remote Sensing Image Classification with Large Scale Gaussian Processes
• Rayleigh Regression Model for Ground Type Detection in SAR Imagery
• Parametric and Nonparametric Tests for Speckled Imagery
• Signal Detection and Inference Based on the Beta Binomial Autoregressive Moving Average Model