Model-Driven Deep Pipeline With Uncertainty-Aware Bundle Adjustment for Satellite Photogrammetry

Bundle adjustment (BA), a vital technology in satellite photogrammetry, directly determines the quality of geographic information mapping. However, the existing BA methods suffer from bottlenecks in the cases of limited stereo views caused by input guidance inadequacy and biased modeling. To conquer these issues, a model-driven satellite photogrammetry deep pipeline (SPDP) is proposed in this article. Specifically, for the triplets of remote sensing images (RSIs), the fusion feature maps are extracted by our attention-driven multiscale feature extractor (AMFE), which emphasizes the image information and provides guidance for the subsequent multiview geometric processing. Following that, with the feature error volume as input, a dedicated feature-metric error perceptron module (FEPM) is built to infer the observation uncertainty and predict the pixel-wise compensations. Furthermore, a novel uncertainty-aware BA (UBA) is implemented to derive accurate and robust 3-D point clouds, which introduces the BA model transformation and the specialized iterative refinement to enhance the observation error elimination capability. The detailed experimental results demonstrate the feasibility and effectiveness of the proposed pipeline, which is significant for remote sensing surveys and mapping.