Existing NeRF models for satellite images suffer from slow speeds, mandatory solar information as input, and limitations in handling large satellite images. In response, we present SatensoRF, which significantly accelerates the entire process while employing fewer parameters for satellite imagery of large size. Besides, we observed that the prevalent assumption of Lambertian surfaces in neural radiance fields falls short for vegetative and aquatic elements. In contrast to the traditional hierarchical MLP-based scene representation, we have chosen a multiscale tensor decomposition approach for color, volume density, and auxiliary variables to model the lightfield with specular color. Additionally, to rectify inconsistencies in multi-date imagery, we incorporate total variation loss to restore the density tensor field and treat the problem as a denosing task.To validate our approach, we conducted assessments of SatensoRF using subsets from the spacenet multi-view dataset, which includes both multi-date and single-date multi-view RGB images. Our results clearly demonstrate that SatensoRF surpasses the state-of-the-art Sat-NeRF series in terms of novel view synthesis performance. Significantly, SatensoRF requires fewer parameters for training, resulting in faster training and inference speeds and reduced computational demands.

翻译：现有针对卫星图像的NeRF模型存在速度慢、需要强制输入太阳信息以及难以处理大规模卫星图像等问题。为此，我们提出SatensoRF，该模型在显著加速整个流程的同时，对大尺寸卫星图像使用更少的参数。此外，我们观察到神经辐射场中普遍采用的朗伯面假设对植被和水体要素并不适用。与传统的层级式MLP场景表示不同，我们选择多尺度张量分解方法来处理颜色、体密度及辅助变量，从而对具有镜面反射颜色的光场进行建模。同时，为修正多日期图像中的不一致性，我们引入全变分损失来恢复密度张量场，并将该问题视为去噪任务。为验证方法的有效性，我们使用Spacenet多视角数据集中的子集对SatensoRF进行评估，该数据集包含多日期和单日期多视角RGB图像。结果清晰表明，SatensoRF在新视角合成性能上超越了当前最先进的Sat-NeRF系列。值得注意的是，SatensoRF训练所需参数更少，从而实现了更快的训练与推理速度，并降低了计算需求。