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Ship detection by satellite remote sensing is of great significance for the safety of maritime navigation and the maintenance of maritime rights and interests. The traditional ship detection based on high spatial resolution synthetic aperture radar (SAR) and optical satellite images is difficult to realize high-frequency monitoring application due to the long revisit period. Objective: The medium resolution Coastal Zone Imager (CZI) carried by China"s "Ocean-1" series satellites (HY-1) has a relatively low spatial resolution (50m), but HY-1C and HY-1D form a double satellite network observation in the morning and afternoon, which has the advantage of short revisit period and is of great value for marine vessel monitoring. We try to realize the ship automatic detection and orientation technology of medium-resolution CZI images, which will be of great value to the monitoring of ships at sea. Method: In this study, convolutional neural network is used for feature learning and target extraction, and an automatic ship detection method of HY-1/CZI image is established. Result: The verification results show that compared with the traditional image processing methods, this method has the characteristics of no need to adjust the threshold and strong adaptability, and the detection accuracy reaches 77.71%, which can be applied to the automatic monitoring of marine moving ships in HY-1/CZI image. Conclusion: The algorithm in this paper can directly detect the position and motion information of marine moving ships from the medium-resolution HY-1/CZI image, without manual screening, realize the automatic extraction of wake, and overcome the problem of insufficient resolution of the medium-resolution optical image. On the basis of the detection results of wake detection, this paper further describes the wake quantitatively, and obtains the information of the ship"s position and movement direction.