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 paper, we propose an automated pipeline named CraterNet to detect craters on the Moon and Mercury from 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 paper 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.

Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively.

In order to enhance the efficiency of the image transmission system and the robustness of the optical imaging system of the Association of Sino-Russian Technical Universities satellite, a new framework of on-board cloud detection by utilizing a lightweight U-Net and JPEG compression strategy is described. In this method, a careful compression strategy is introduced and evaluated to acquire a balanced result between the efficiency and power consuming. A deep-learning network combined with lightweight U-Net and Mobilenet is trained and verified with a public Landsat-8 data set Spatial Procedures for Automated Removal of Cloud and Shadow. Experiment results indicate that by utilizing image-compression strategy and depthwise separable convolutions, the maximum memory cost and inference speed are dramatically reduced into 0.7133 Mb and 0.0378 s per million pixels while the overall accuracy achieves around 93.1%. A good possibility of the on-board cloud detection based on deep learning is explored by the proposed method.

With the continuous advancement of deep space exploration missions, the solar system boundary exploration mission is established as one of the China's most important deep space scientific exploration missions. The mission of the solar system boundary exploration has many challenges such as ultra-remote detection distance, ultra-long operation time, and ultra-long communication delay. Therefore, the problem of high-precision autonomous navigation needs to be solved urgently. This paper designs an autonomous intelligent navigation method based on X-ray pulsars in the cruise phase, which estimate the motion state of the probe in real time. The proposed navigation method employs the Q-learning Extended Kalman filter (QLEKF) to improve navigation accuracy during long periods of self-determining running. The QLEKF selects automatically the error covariance matrix parameter of the process noise and the measurement noise by the reward mechanism of reinforcement learning. Compared to the traditional EKF and AEKF, the QLEKF improves the estimation accuracy of position and velocity. Finally, the simulation result demonstrates the effectiveness and the superiority of the intelligent navigation algorithm based on QLEKF, which can satisfy the high-precision navigation requirements in the cruise phase of the solar system boundary exploration.