Impact Crater Detection on Mars Digital Elevation and Image Model

As outer space image acquisition techniques progress, larger amounts of planetary data sets become available. Impact crater statistics about planets is an important resource as use of this information reveals geological history. Since manual detection of impact craters requires substantial human resource, there is a compelling need to investigate automated crater detection algorithms. In this study, we develop a novel framework to detect Martian impact craters by fusing data obtained from Mars Global Surveyor. In our proposed method, extracted craters from Mars Digital Image Model (MDIM) are crosschecked by using Mars Digital Elevation Model (MDEM). Multi population genetic algorithm (MPGA) has been devised to extract craters from scale invariant feature set found by SIFT algorithm. In order to decrease the number of false positives, extracted from MDIM are validated by detected basins from MDEM. Experimental results on NASA databases suggest high crater detection rates.