Ilow J, Leung H
IEEE Trans Image Process. 2001;10(5):792-7. doi: 10.1109/83.918571.
A texture model for synthetic aperture radar (SAR) images is presented. Specifically, a sea surface in satellite images is modeled using the two-dimensional (2-D) fractionally integrated autoregressive-moving average (FARIMA) process with a non-Gaussian white driving sequence. The FARIMA process is an ARMA type model which is asymptotically self-similar. It captures the long-range as well as short-range spatial dependence structure of an image with a small number of parameters. To estimate these parameters, an efficient estimation procedure based on a spectral fit is presented. Real-life ocean surveillance radar images collected by the RADARSAT sensor are used to evaluate the practicality of this FARIMA approach. Using the radial power spectral density, the new model is shown to provide a more accurate description of the SAR images than the conventional moving-average (MA), autoregressive (AR), and fractionally differenced (FD) models.
提出了一种合成孔径雷达（SAR）图像的纹理模型。具体而言，利用具有非高斯白驱动序列的二维（2-D）分数积分自回归移动平均（FARIMA）过程对卫星图像中的海面进行建模。FARIMA过程是一种渐近自相似的自回归滑动平均（ARMA）类型模型。它用少量参数捕捉图像的长程和短程空间依赖结构。为了估计这些参数，提出了一种基于频谱拟合的有效估计程序。利用RADARSAT传感器收集的实际海洋监视雷达图像来评估这种FARIMA方法的实用性。通过径向功率谱密度表明，新模型比传统的移动平均（MA）、自回归（AR）和分数差分（FD）模型能更准确地描述SAR图像。
