Detecting Impact Craters in Planetary Images Using Machine Learning

Introduction

Impact craters are structures formed by collisions of meteoroids with planetary surfaces. They are common features on all hard-surface bodies in the Solar System, but are most abundant on bodies such as the Moon, Mercury, or Mars where they can accumulate over geologically long times due to slow surface erosion rates. The importance of impact craters stems from the wealth of information that detailed analysis of their distributions and morphology can bring forth. In particular, in the absence of in situ measurements, crater counting is the only technique for establishing relative chronology of different planetary surfaces (Wise & Minkowski, 1980) (Tanaka, 1986). Simply put, heavily cratered surfaces are relatively older than less cratered surfaces. Thus, surveying impact craters is one of the most fundamental tools of planetary geology science (Hartmann, Martian cratering VI: Crater count isochrons and evidence for recent volcanism from Mars Global Surveyor, 1999) (Hartmann & Neukum, 2001).

Presently, all such surveys are done manually via visual inspection of images. Manually compiled databases of craters are either spatially comprehensive, but restricted to only the largest craters (Barlow, 1988) (Rodionova, et al., 2000) (Andersson & Whitaker, 1982) (Kozlova, Michael, Rodinova, & Shevchenko, 2001), or size comprehensive, but limited to only narrowly defined geographical locations. Using spatially comprehensive catalogs of only the largest craters allows for establishing relative chronology on large spatial scale and coarse temporal resolution. This is because large craters are rare, so their counts must be collected from spatially extended regions in order to accumulate a sufficient number of samples for accurate statistics (cumulative distribution of crater sizes is well approximated by a power law with index equal to -2). A finer spatial resolution of the stratigraphy can only be obtained from statistics of much more numerous smaller craters. Compiling global or regional catalogs of small craters, however, would be a very laborious process, ill-suited for the standard technique of manual visual detection.

Advances in gathering planetary data by space probes has resulted in a deluge of high resolution images that show craters as small as 100 m in diameter, and can be combined into mosaics covering entire surfaces of planets such as Mars, the Moon, and soon the Mercury. It is now clear that, if left to manual surveys, the fraction of cataloged craters to the craters actually present in the available and forthcoming imagery data will continue to drop precipitously. Progress in measuring surface relative chronology with increasing spatial and temporal accuracies can only be achieved by automating the process of crater surveying. Because of the importance of craters to the field of planetary science, there have been numerous attempts to develop a “crater detection algorithm” (CDA). Despite a large body of work, practitioners of planetary science continue to count craters manually, resulting in a lack of progress (relative to available data) in improving the surface chronology. This is because most approaches to CDA are restricted to demonstration that a particular algorithm achieves high accuracy on a particular image or set of images containing relatively simple “textbook” craters, whereas practitioners of planetary science are looking for a robust algorithm having a decent performance on all possible surfaces. In reality, craters are rarely simply circular on a relatively uniform background; craters appearance in an image depends on their level of degradation, on their internal morphologies, on the degree of overlapping with other craters, on image quality (illumination angle, surface properties, atmospheric state), and on their sizes, that may differ by orders of magnitude. Thus the construction of a robust and practical CDA stands as a significant challenge to the scientific community.