Principal Component Analysis for Lunar Crater Detection
This work addresses the need for automated optical navigation in lunar missions by improving crater template generation, but the results are limited to simulated data and the method is incremental.
The authors propose EigenCrater, a method using principal component analysis on crater DEMs to generate templates for lunar crater detection, achieving superior detection and position estimation compared to hand-picked templates on simulated imagery.
Optical navigation is a critical component for lunar orbiter and lander missions. Image-based crater identification has emerged as a promising technology for optical navigation due to the abundance of craters on the lunar surface and the availability of extensive crater catalogs. Moreover, due to the relative morphological homogeneity among lunar craters, template matching has been identified as a promising approach for identification. In this paper, we propose EigenCrater, an automated crater template generation method based on principal component analysis of crater digital elevation maps (DEMs). We demonstrate superior detection and position estimation performance relative to hand-picked templates on simulated lunar imagery.