发表机构
Aerospace Information Research Institute, Chinese Academy of Sciences; International Research Center of Big Data for Sustainable Development Goals; University of Chinese Academy of Sciences（中国科学院空天信息创新研究院; 国际可持续发展大数据研究中心; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于3D高斯溅射的BRF-GS框架，通过混合BRDF驱动核、两阶段训练策略等，结合AIR-BRF数据集，实现高保真的BRF建模与多角度高光谱反射率图像生成。
AI 中文摘要
双向反射因子（BRF）表征地表的方向辐射特性，但现有三维（3D）辐射传输模型需复杂场景构建与计算密集型辐射传输求解器，限制了多角度高光谱反射率图像的高效生成。3D高斯溅射（3DGS）为神经场景表示与新视图合成提供了高效框架，但其低阶球谐表示不足以应对复杂方向反射，且高光谱数据的高维性与波段间质量差异带来额外挑战。为解决这些问题，本文提出BRF-GS，一种基于3DGS的BRF建模与高光谱反射率图像生成框架。BRF-GS引入混合双向分布函数（BRDF）驱动的核来表征复杂方向反射，选择几何可靠的光谱波段进行鲁棒的3D场景初始化，并采用将几何优化与光谱建模解耦的两阶段训练策略。本文进一步构建了AIR-BRF数据集，该多角度高光谱方向反射数据集包含3个具有不同自然与人工目标的场景。实验表明，BRF-GS实现了优异的空间与光谱保真度，并准确复现了特征性的依赖视图的BRF响应。所提框架为遥感场景中的BRF建模与多角度高光谱反射率图像生成提供了一种高效的数据驱动方法。
英文摘要
The bidirectional reflectance factor (BRF) characterizes the directional radiative properties of terrestrial surfaces. However, existing three-dimensional (3D) radiative transfer models require complex scene construction and computationally intensive radiative transfer solvers, limiting efficient generation of multi-angle hyperspectral reflectance imagery. 3D Gaussian Splatting (3DGS) offers an efficient framework for neural scene representation and novel view synthesis, but its low-order spherical harmonics representation is insufficient for complex directional reflectance, while the high dimensionality and inter-band quality differences of hyperspectral data introduce additional challenges. To address these challenges, we propose BRF-GS, a 3DGS-based framework for BRF modeling and hyperspectral reflectance image generation. BRF-GS introduces a hybrid BRDF-driven kernel to represent complex directional reflectance, selects geometry-reliable spectral bands for robust 3D scene initialization, and adopts a two-stage training strategy that decouples geometry optimization from spectral modeling. We further construct the AIR-BRF dataset, a multi-angle hyperspectral directional reflectance dataset comprising three scenes with diverse natural and artificial targets. Experiments demonstrate that BRF-GS achieves superior spatial and spectral fidelity and accurately reproduces characteristic view-dependent BRF responses. The proposed framework provides an efficient data-driven approach for BRF modeling and multi-angle hyperspectral reflectance image generation in remote sensing scenes.
Comments58 pages, 10 figures, 4 tables
