Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting

Abstract

Identifying impact craters on planetary surfaces is one fundamental task in planetary science. In this paper, we present an embedded framework on auto-detection of craters, using feature selection and boosting strategies. The paradigm aims at building a universal and practical crater detector. This methodology addresses three issues that such a tool must possess: (i) it utilizes mathematical morphology to efficiently identify the regions of an image that can potentially contain craters; only those regions, defined as crater candidates, are the subjects of further processing; (ii) it selects Haar-like image texture features in combination with boosting ensemble supervised learning algorithms to accurately classify candidates into craters and non-craters; (iii) it uses transfer learning, at a minimum additional cost, to enable maintaining an accurate auto-detection of craters on new images, having morphology different from what has been captured by the original training set. All three aforementioned components of the detection methodology are discussed, and the entire framework is evaluated on a large test image of 37,500 x 56,250$ m2 on Mars, showing heavily cratered Martian terrain characterized by nonuniform surface morphology. Our study demonstrates that this methodology provides a robust and practical tool for planetary science, in terms of both detection accuracy and efficiency.