Synthetic aperture radar (SAR) systems are efficient to deal with remote sensing issues. In contrast, SAR images are affected by speckle noise, due to the use of coherent illumination in their capturing. This noise imposes both a granular interference on such images (precluding their interpretability) and a multiplicative and non-Gaussian nature on their data. The multiplicative modelling is often used to surpass previous difficulties, mainly its particular case the ${\mathcal{G}}_I^0$ distribution. In this paper, we introduce a new time series modelling for SAR imagery, called ${\mathcal{G}}_I^0$ autoregressive-moving-average (ARMA) process. We derive some of its mathematical properties: score vector, Fisher information matrix, residual analysis and prediction equations. The maximum likelihood estimation procedure for ${\mathcal{G}}_I^0$-ARMA parameters is discussed and some asymptotic behaviours for its estimates are quantified by Monte Carlo experiments. Applications to Munich and Foulum SAR actual images are made. Results show the proposed model can outperform the Γ-ARMA model.

合成孔径雷达 (SAR) 系统可有效处理遥感问题。相比之下，SAR 图像受散斑噪声的影响，因为在其捕获中使用了相干照明。这种噪声既对此类图像施加了粒度干扰（排除了它们的可解释性），又对其数据施加了乘法和非高斯性质。乘法建模经常被用来克服以前的困难，主要是它的特殊情况${\mathcal{G}}_{\mathrm{一世}}^0$分配。在本文中，我们介绍了一种新的 SAR 图像时间序列建模，称为${\mathcal{G}}_{\mathrm{一世}}^0$自回归移动平均 (ARMA) 过程。我们推导出它的一些数学属性：得分向量、Fisher 信息矩阵、残差分析和预测方程。最大似然估计过程${\mathcal{G}}_{\mathrm{一世}}^0$讨论了 ARMA 参数，并通过蒙特卡罗实验量化了其估计的一些渐近行为。对慕尼黑和 Foulum SAR 实际图像进行了应用。结果表明，所提出的模型可以胜过Γ-ARMA 模型。