Abstract
Prompted by crater counts as the only available tool for measuring remotely the relative ages of geologic formations on planets, advances in remote sensing have produced a very large database of high resolution planetary images, opening up an opportunity to survey much more numerous small craters improving the spatial and temporal resolution of stratigraphy. Automating the process of crater detection is key to generate comprehensive surveys of smaller craters. Here, the authors discuss two supervised machine learning techniques for crater detection algorithms (CDA): identification of craters from digital elevation models (also known as range images), and identification of craters from panchromatic images. They present applications of both techniques and demonstrate how such automated analysis has produced new knowledge about planet Mars.
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