Automatic detection of sub-km craters in high resolution planetary images

Impact craters are among the most studied geomorphic planetary features because they yield information about the past geological processes and provide a tool for measuring relative ages of observed geologic formations. Surveying impact craters is an important task which traditionally has been achieved by means of visual inspection of images. The shear number of smaller craters present in high resolution images makes visual counting of such craters impractical. In this paper we present a method that brings together a novel, efficient crater identification algorithm with a data processing pipeline; together they enable a fully automatic detection of sub-km craters in large panchromatic images. The technical details of the method are described and its performance is evaluated using a large, 12.5 m/pixel image centered on the Nanedi Valles on Mars. The detection percentage of the method is ∼70%. The system detects over 35,000 craters in this image; average crater density is , but localized spots of much higher crater density are present. The method is designed to produce “million craters” global catalogs of sub-km craters on Mars and other planets wherever high resolution images are available. Such catalogs could be utilized for deriving high spatial resolution and high temporal precision stratigraphy on regional or even planetary scale.