提出首个用于斑点图像的伽马分布回归模型，可预测极化通道间强度关系。
Regression Model for Speckled Data with Extremely Variability
- 基于$\/mathcal{G}^0_I$分布构建回归模型，建模多极化通道强度关系。
- 模拟与实测数据验证：模型能有效捕捉斑点图像中通道间依赖性。
- 适用于遥感图像分析，尤其适合处理带乘性噪声的SAR数据
合成孔径雷达（SAR）是高效的遥感工具，但其图像数据受斑点噪声污染，无法满足加性正态噪声假设。目前最成功的描述方法是乘性模型，其中强度服从具有正支持的分布，$\\mathcal{G}^0_I$模型尤为突出。尽管已有多种参数估计方法，但尚无研究探讨该模型的回归结构。本文提出$\\mathcal{G}^0_I$回归模型，用于描述其他极化通道强度对目标强度的影响。推导了该模型的费舍尔信息矩阵、残差度量及影响工具等理论性质，并提出最大似然点估计与区间估计方法，通过蒙特卡洛实验评估性能。仿真与真实数据结果表明，该模型在SAR图像分析中具有实用价值。
原文摘要 · Abstract (English)
Synthetic aperture radar (SAR) is an efficient and widely used remote sensing tool. However, data extracted from SAR images are contaminated with speckle, which precludes the application of techniques based on the assumption of additive and normally distributed noise. One of the most successful approaches to describing such data is the multiplicative model, where intensities can follow a variety of distributions with positive support. The $\mathcal{G}^0_I$ model is among the most successful ones. Although several estimation methods for the $\mathcal{G}^0_I$ parameters have been proposed, there is no work exploring a regression structure for this model. Such a structure could allow us to infer unobserved values from available ones. In this work, we propose a $\mathcal{G}^0_I$ regression model and use it to describe the influence of intensities from other polarimetric channels. We derive some theoretical properties for the new model: Fisher information matrix, residual measures, and influential tools. Maximum likelihood point and interval estimation methods are proposed and evaluated by Monte Carlo experiments. Results from simulated and actual data show that the new model can be helpful for SAR image analysis.
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