Automated Registration of Full Moon Remote Sensing Images Based on Triangulated Network Constraints

Abstract

The registration of full-moon remote sensing images constitutes a pivotal stage in the fusion analysis of multiple lunar remote sensing datasets. Addressing prevailing issues in automatic registration, such as the broad width of full-moon data, significant internal distortion, and texture distortion in high-latitude regions, this paper proposes a method for automatic matching and correction based on triangulation constraints. The approach employs a matching strategy progressing from coarse to fine and from sparse to dense. It optimizes and combines multiple existing matching algorithms, enhances the extraction of initial network points, constructs irregular triangulation networks using these points, conducts dense matching with each triangulation network as a basic unit, and introduces a geometric correction method based on triangulation network + grid (TIN + GRID) for the registration of full-moon data. For the matching of full-moon remote sensing images in high-latitude regions, a novel approach involving memory projection forward transformation-matching-projection inverse transformation is adopted. Through registration experiments with full-moon image data and an analysis of registration accuracy at different latitudes, the average mean square error is found to be less than 2 pixels. These results signify the efficacy of the proposed method in effectively addressing the automatic registration challenges encountered in full-moon remote sensing images.