Crater detection robust to illumination and shape changes using convolutional neural network

As a vast amount of data with respect to the moon and Mars is collected, exploration missions are shifting to the next step, the aim of which is a precise landing on a predetermined target. A promising technology for precision landing is terrain relative navigation (TRN), which collates landmarks detected from images and maps of landmarks. Crater detection is one of the essential technologies for TRN. A problem in detecting craters is the apparent change in craters due to illumination conditions. Another problem is the change in shape due to crater degradation. We propose a novel crater detection method based on combining a support vector machine (SVM) and a convolutional neural network (CNN) to make detection performance robust against apparent change. In the linear SVM, gradient images of a crater image dataset are learned. The learned classifier is then used to calculate the objectness score for region proposal. Next, the CNN identifies the image of the proposed region as to whether or not it is a crater. Our results show that the proposed method can detect craters in a wide range of illumination and shape conditions, and has better average precision than traditional crater detectors.