论文标题
SAR图像建模的2-D Rayleigh自回旋运动平均模型
2-D Rayleigh Autoregressive Moving Average Model for SAR Image Modeling
论文作者
论文摘要
通常应用二维（2-D）自回归移动平均值（ARMA）模型来描述现实世界图像数据，通常假设高斯或对称噪声。但是，现实世界中的数据通常呈现非高斯信号，具有不对称的分布和严格的正值。特别是，已知SAR图像以雷利分布为特征。在这种情况下，引入了针对2D雷利分布的数据量身定制的ARMA模型 - 2-D RARMA模型。得出了2-D RARMA模型，并讨论了有条件的可能性推断。提出的模型已提交给广泛的蒙特卡洛模拟，以评估有条件的最大似然估计器的性能。此外，在SAR图像处理的背景下，进行了两个全面的数值实验，将所提出模型的异常检测和图像建模结果与传统的2-D ARMA模型和文献中的竞争方法进行了比较。
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.
