Automatic crater detection and age estimation for mare regions on the lunar surface. Salih, A. L., Schulte, P., Grumpe, A., Wöhler, C., & Hiesinger, H. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 518-522, Aug, 2017.
Automatic crater detection and age estimation for mare regions on the lunar surface [pdf]Paper  doi  abstract   bibtex   
In this paper, we investigate how well an automatic crater detection algorithm is suitable to determine the surface age of different lunar regions. A template-based crater detection algorithm is used to analyze image data under known illumination conditions. For this purpose, artificially illuminated crater templates are used to detect and count craters and their diameters in the areas under investigation. The automatic detection results are used to obtain the crater size-frequency distribution (CSFD) for the examined areas, which is then used for estimating the absolute model age (AMA) of the surface. The main focus of this work is to find out whether there exists an ideal sensitivity value for automatic crater detection to obtain smallest possible errors between the automatically derived AMA and a reference AMA derived from manually detected craters. The detection sensitivity threshold of our crater detection algorithm (CDA) is calibrated based on five different regions in Mare Cognitum on the Moon such that the age inferred from the manual crater counts corresponds to the age inferred from the CDA results. The obtained best detection threshold value is used to apply the CDA algorithm to another five regions in the lunar Oceanus Procellarum region. The accuracy of the method is examined by comparing the calculated AMAs with the manually determined ones from the literature. It is shown that the automatic age estimation yields AMA values that are generally consistent with the reference values with respect to the one standard deviation errors.

Downloads: 0