  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.
