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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 imagecompression 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.

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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 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.

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In order to enhance the accuracy and the robustness of the attitude determination and control system in observation satellites, a new way to fuse gyro and star tracker measurement with image registration is described. In this method, a novel and complete framework is proposed to estimate the on-orbit attitude variations from multi-spectrum remote sensing images. An extended Kalman filter is derived to calibrate the gyro bias drift and the star tracker error. The new framework is tested with realistically simulated data and remote sensing images based on JL-1 satellite. Simulation and experiment results indicate that based on the image registration, the satellite attitude variations could be detected in real time and applied for the accurate gyro and star tracker bias calibration.

Both Global Navigation Satellite Systems and X-ray pulsar-based navigation system use relative position information to provide accurate spacecraft navigation. To supplement that navigation information, an absolute reference position from other types of measurements should be provided; however, that may increase the complexity or decrease the autonomy of the navigation system. To overcome those drawbacks, this paper proposes an initial orbit determination method using only relative position increment measurements and their corresponding epoch. The proposed method can be summarised as three steps: first, determining the orbital plane from a set of relative position vectors; next, determining the shape of the orbit by solving an ellipse fitting problem in two dimensions; finally, estimating the absolute position and all six orbital elements according to Kepler's equation and geometric relations. Monte Carlo simulations were done to analyse the feasibility and features of the proposed method. The results show that the algorithm we used does not need an initial guess and is robust. It can obtain an acceptable result from a 20% portion of an orbit when the signal-to-noise ratio is set at 15 dB with determining the inclination and right ascension of the ascending node quickly and accurately.