Planet Craters Detection Based on Unsupervised Domain Adaptation

The detection and localization of craters on the Moon and other planets play an essential role in planet landing, spacecraft navigation, and geologic study. Historically, craters detection involves manually measuring the size and placement of craters in surface images. In this article, we propose an automated pipeline named CraterNet to detect craters on the Moon and Mercury from digital elevation map (DEM) images. Firstly, the convolutional neural network (CNN) based object detection model is trained and tested to detect the craters in a supervised manner. To address the domain discrepancy between the source and target data, an unsupervised domain adaptation (UDA) approach combined with domain randomization is suggested. A causal inference-based feature matching (CIFM) approach integrated with histogram matching is then developed to improve the effectiveness the unsupervised crater detection. The DeepMoon crater dataset and the unsupervised Mercury crater DEMs are introduced in this article to illustrate the applicability and efficacy of the designed method. Results indicate that: 1) The developed approach demonstrates a high performance of supervised crater detection on the DeepMoon dataset, with the F1 and AP scores of 0.786 and 0.804, respectively. 2) The detection model is transferred and performs well on the Mercury dataset in which craters are of different sizes and shapes, as the Precision, Recall, and F1 score for ellipse-shape craters are 0.734, 0.773, and 0.753, respectively. 3) The proposed CraterNet outperforms other deep learning-based segmentation and detection models in terms of crater detection and localization scores.