Crater Detection

Courtesy: James Stuby (NASA) / Wikimedia Commons

A crater is a structure or geological formation that is produced as a result of impact of a smaller body upon a larger heavenly body. Craters are found on all solid heavenly bodies such as Earth, the Moon, Mars, Mercury, Venus etc. 

Studying craters is important because they are time capsules to how our Solar System evolved and gives us clues to the past, present and future of the evolution of planetary bodies without humans setting foot on them. Crater densities have been correlated with surface age and crater counts have been used used to estimate the relative age of the lunar surface. There are many other uses to studying craters, so it is important to be able to detect craters quickly in an automated fashion.

This research uses a novel method of detecting craters by using multiple co-registered data sources: optical images, digital elevation maps (DEMs) and slope maps of the lunar surface. The method uses a deep learning architecture known as Mask R-CNN to detect and identify craters.

In terms of performance, this new system outperforms or matches the current state-of-the-art. With high recall value for one data catalog and medium F1-score ranges for another data catalog, this method looks promising for future exploration. The system was also evaluated on its generalization capability ie. training on different input data and testing on different input data. So training on one kind of images for the lunar surface (optical images and DEMs) also produces good detections of craters on the Martian surface from entirely different data type (thermal IR images).

For a detailed look at this advancement, go here.