A general volume scattering model was proposed by Neumann et al. based on anisotropy and orientation randomness, which has been successfully applied to forest parameter retrieval from polarimetric interferometric synthetic aperture radar (SAR). A general implementation of the Neumann volume scattering model is designed for fully polarimetric SAR data, incorporating the contributions from the ground level consisting of surface scattering and double-bounce scattering. This combination helps to apply the Neumann volume scattering model to different vegetation scenarios. Based on this, a new parameter inversion framework is developed to estimate the anisotropy and the orientation randomness of the volumetric media, which are expected to be consistent with the ground truth. Meanwhile, the polarimetric information from the ground responses is extracted so that the interaction process between the SAR signals and the ground objects is fully explored. The L-band AIRSAR image covering Flevoland, The Netherlands was selected for the experiments. Compared with Neumann decomposition, the parameters estimated by the proposed method can more realistically reflect the canopy scattering characteristics. Moreover, the classification accuracy of crops is effectively improved by the feature parameters obtained by the proposed method.

中文翻译：

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PolSAR 数据的 Neumann 体散射模型的一般实现

Neumann 等人提出了一个通用的体积散射模型。基于各向异性和方向随机性的方法，已成功应用于极化干涉合成孔径雷达（SAR）的森林参数反演。Neumann 体散射模型的一般实现是为全极化 SAR 数据设计的，其中包含来自地面的贡献，包括表面散射和双反弹散射。这种组合有助于将 Neumann 体积散射模型应用于不同的植被场景。在此基础上，开发了一种新的参数反演框架来估计体积介质的各向异性和方向随机性，这有望与基本事实一致。同时，从地面响应中提取极化信息，充分探索SAR信号与地物的交互过程。选择覆盖荷兰弗莱沃兰的 L 波段 AIRSAR 图像进行实验。与Neumann分解相比，该方法估计的参数更能真实地反映冠层散射特性。此外，该方法得到的特征参数有效地提高了农作物的分类精度。该方法估计的参数更能真实地反映冠层散射特性。此外，该方法得到的特征参数有效地提高了农作物的分类精度。该方法估计的参数更能真实地反映冠层散射特性。此外，该方法得到的特征参数有效地提高了农作物的分类精度。