Currently, deep neural networks have gained impressive performance for object detection when plenty of labeled training data are available. However, a substantial quantity of labeled data is typically rare and time-consuming to obtain in deep space exploration. To possibly alleviate this problem, we use domain adaptation (DA) to effectively detect unannotated real data samples in the crater detection problem with the help of autoannotated synthetic data samples only. Specifically, we present a novel network, namely, CraterDANet, which unifies image- and feature-level adversarial DAs to bridge the domain gap between synthetic and real data. To evaluate our method, we present a new lunar crater dataset that contains nearly 20 000 small-scale craters captured from different Lunar Reconnaissance Orbital (LRO) Narrow Angle Camera (NAC) grayscale images that have large variations in shape, size, overlap, degradation, and illumination conditions. To the best of our knowledge, this is the first lunar crater dataset with diversification of illumination conditions, which provides bounding box annotations. Through experiments, the proposed CraterDANet, which was trained on labeled synthetic data without using any additional real labeled data, achieved an $F1$ -score of 80.27%, which is slightly better than traditional supervised methods trained on real data. Although the performance still falls short of the state-of-the-art Faster R-convolutional neural network (CNN), it achieves an $F1$ -score of +5.98% performance improvement compared with Faster R-CNN trained on synthetic data only. This result demonstrates the effectiveness of the proposed method for reducing the domain gap between synthetic and real data.

Tianwen-1 is the first Mars probe launched by China and the first mission in the world to successfully complete the three steps of exploration (orbiting, landing, and roving) at the one time. Based on the unverifiable descent images which cover the full range of the landing area, trajectory recovery and fine terrain reconstruction are important parts of the planetary exploration process. In this paper, a novel trajectory recovery and terrain reconstruction (TR-TR) algorithm employing descent images is proposed for the dual-restrained conditions: restraints of the flat terrain resulting in an unstable solution of the descent trajectory and of the parabolic descent trajectory causing low accuracy of terrain reconstruction, respectively. A landing simulation experiment on a landing field with Mars-like landform was carried out to test the robustness and feasibility of the algorithm. The experiment result showed that the horizontal error of the recovered trajectory didn’t exceed 0.397 m, and the elevation error of the reconstructed terrain was no more than 0.462 m. The algorithm successfully recovered the descent trajectory and generated high-resolution terrain products using in-orbit data of Tianwen-1, which provided effective support for the mission planning of the Zhurong rover. The analysis of the results indicated that the descent trajectory has parabolic properties. In addition, the reconstructed terrain contains abundant information and the vertical root mean square error (RMSE) of ground control points is smaller than 1.612 m. Terrain accuracy obtained by in-orbit data is lower than that obtained by field experiment. The work in this paper has made important contributions to the surveying and mapping of Tianwen-1 and has great application value.