Efficient camera self-calibration method for remote sensing photogrammetry

Internal parameter calibration of remote sensing cameras (RSCs) is a necessary step in remote sensing photogrammetry. On-orbit camera calibration widely adopts external ground control points (GCPs) to measure its internal parameters. However, accessible and available GCPs are not easy to achieve when cameras work on a satellite platform. In this paper, we propose an efficient camera self-calibration method using a micro-transceiver in conjunction with deep learning. A supervised learning set is produced by the micro-transceiver, where multiple two-dimensional diffraction grids are produced and transformed into multiple auto-collimating sub-beams equivalent to infinite target-point training examples. A deep learning network is used to invert the learnable internal parameters. The micro-transceiver can be easily integrated into the internal structure of RSCs allowing to calibrate them independently on external ground control targets.