Learning to Detect Small Impact Craters

Machine learning techniques have shown considerable promise for visual inspection tasks such as locating human faces in cluttered scenes. In this paper, we examine the utility of such techniques for the scientifically-important problem of detecting and cataloging impact craters in planetary images gathered by spacecraft. Various supervised learning algorithms, including ensemble methods (bagging and AdaBoost with feed-forward neural networks as base learners), support vector machines (SVM), and continuously-scalable template models (CSTM), are employed to derive crater detectors from ground-truthed images. The resulting detectors are evaluated on a challenging set of Viking Orbiter images of Mars containing roughly one thousand craters. The SVM approach with normalized image patches provides detection and localization performance closest to that of human labelers and is shown to be substantially superior to boundary-based approaches such as the Hough transform.