Time-Based Rational Function Model for Block Adjustment of Long-Strip LROC NAC Images Using Sparse Elevation Controls

A rational function model (RFM) has long been a versatile tool in image block adjustment when building the global control network in lunar topographic mapping. By unifying the physical imaging model with mathematical expressions, the RFM facilitates using multisource images from various missions. However, the scarcity of ground control points (GCPs) on the lunar surface and the imaging void of Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) limits the application of RFM in large-scale mapping tasks. This work proposed a time-based RFM generation approach for strip-based image block adjustment using merely sparse control points. The proposed time-based RFM integrates separated image pieces in the same orbit into a cohesive whole for strip-based block adjustment, requiring merely sparse control points when mapping and geopositioning all the image pieces. Besides, we employ the elevation control information extracted from Lunar Orbit Laser Altimeter (LOLA) data in the strip-based block adjustment. Experiment results indicate that under the same sparse control conditions, the elevation accuracy using strip-based adjustment is improved by an average of 12.8% compared with single-based adjustment, and our method achieves a similar adjustment accuracy using sparse elevation controls as that in the densely controlled single-based block adjustment.