MOVING SHIP DETECTION AND MOVEMENT PREDICTION IN REMOTE SENSING VIDEOS
- 1Department of Aerospace Information Engineering, School of Astronautics, Beihang University, 102206 Beijing, China
- 2Beijing Key Laboratory of Digital Media, 102206 Beijing, China
- 3Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, 102206 Beijing, China
Keywords: Ship Detection, Movement Prediction, Remote Sensing Videos, Deep Learning
Abstract. With the rapid development of remote sensing technology, it is possible to obtain continuous video data from outer space successfully. It is of great significance in military and civilian fields to detect moving objects from the remote sensing image sequence and predict their movements. In recent years, this issue has attracted more and more attention. However, researches on moving object detection and movement prediction in high-resolution remote sensing videos are still in its infancy, which is worthy of further study. In this paper, we propose a ship detection and movement prediction method based on You-Only-Look-Once (YOLO) v3 and Simple Online and Realtime Tracking (SORT). Original YOLO v3 is improved by multi-frame training to fully utilize the information of continuous frames in a fusion way. The simple and practical multiple object tracking algorithm SORT is used to recognize multiple targets detected by multi-frame YOLO v3 model and obtain their coordinates. These coordinates are fitted by the least square method to get the trajectories of multiple targets. We take the derivative of each trajectory to obtain the real-time movement direction and velocity of the detected ships. Experiments are performed on multi-spectral remote sensing images selected on Google Earth, as well as real multi-spectral remote sensing videos captured by Jilin-1 satellite. Experimental results validate the effectiveness of our method for moving ship detection and movement prediction. It shows a feasible way for efficient interpretation and information extraction of new remote sensing video data.