On Crater Verification Using Mislocalized Crater Regions

Automatic crater detection in planetary images is an important task with many applications in planetary science, spacecraft navigation, landing, and control. Typically, crater detection algorithms consist of two main steps: candidate crater region extraction and crater verification. Various methods have been proposed for extracting candidate crater regions, ranging from detecting circular/elliptical regions to detecting highlight and shadow regions. For crater verification, powerful feature extraction and machine learning techniques have been employed. While this two-step approach can be efficient and robust, inaccuracies in the candidate crater region extraction step can result in mislocalized crater regions which could affect verification performance. In this paper, we investigate the robustness of various feature extraction methods to mislocalized crater regions. Using features which are robust to localization errors but also choosing a more representative training set has yielded significant performance improvements on an extensive dataset from the Lunar Reconnaissance Orbiter (LRO).