# Principal Component Analysis for Lunar Crater Detection

arXiv: https://arxiv.org/abs/2605.17125 · HTML: https://mlsanity.com/paper/2605.17125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 16 May 2026.

## Builds on (most cited first, 8 of 8)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion](https://mlsanity.com/paper/2001.11584/) (2020)
- [Image simulation for space applications with the SurRender software](https://mlsanity.com/paper/2106.11322/) (2021)
- [AstroSLAM: Autonomous Monocular Navigation in the Vicinity of a Celestial Small Body -- Theory and Experiments](https://mlsanity.com/paper/2212.00350/) (2022)
- [Lunar Crater Identification in Digital Images](https://mlsanity.com/paper/2009.01228/) (2020)
- [Absolute Triangulation Algorithms for Space Exploration](https://mlsanity.com/paper/2205.12197/) (2022)

## Abstract (arXiv)

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.