SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6\% can be achieved by using only 12 images per class.

翻译：SAR图像对观测配置高度敏感，且在不同视角下呈现显著差异，这使得其各向异性特征的表示与学习面临挑战。当前深度学习方法在不同视角下的泛化能力普遍较弱。受神经辐射场（NeRF）概念的启发，本研究将SAR成像机理与神经网络相结合，提出了一种面向SAR图像生成的新型NeRF模型。遵循映射与投影原理，通过可微分渲染方程将一组SAR图像隐式建模为三维成像空间中衰减系数与散射强度的函数。进而构建SAR-NeRF以学习体素的衰减系数与散射强度分布，并解析推导了三维体素SAR渲染方程的向量化形式，以及三维空间体素与二维视角射线网格之间的采样关系。通过在多个数据集上的定量实验，全面评估了SAR-NeRF的多视角表示与泛化能力。此外，研究发现经SAR-NeRF增强的数据集可显著提升小样本学习场景下的SAR目标分类性能，仅需每类12张图像即可实现91.6%的十类分类准确率。