Principal Component Analysis for Lunar Crater Detection

TL;DR

This paper introduces EigenCrater, a PCA-based automated method for generating crater templates to improve lunar crater detection and positioning in optical navigation.

Contribution

The paper presents a novel PCA-based approach for automated crater template generation, enhancing detection accuracy over traditional hand-picked templates.

Findings

EigenCrater outperforms hand-picked templates in detection accuracy.

The method achieves superior position estimation in simulated lunar imagery.

PCA effectively captures crater features for template generation.

Abstract

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.