An Improved Kuhn Munkres Algorithm for Ship Matching in Optical Satellite Images

Ship matching based on multiple satellite images can effectively grasp the trajectories of the ship with long time intervals, providing technical support for marine regulation and surveillance. Ship detection and ship association are two steps for ship matching based on satellite images. However, traditional unsupervised moving object detection methods only apply to the scenes where the camera is stationary. In addition, it cannot segment the ships from the background, interfering with the data association step. Most data association algorithms rely on the target's location information and are not suitable for ship matching based on satellite remote sensing images with long time intervals, because the ship's position has significantly changed. Therefore, an unsupervised automatic ship matching method is proposed in this article. In the ship detection part, the ship is segmented based on the proposed preprocessing algorithm and the GrabCut algorithm to remove the interference caused by background movement; in the data association part, the Kuhn Munkres algorithm is improved by similarity comparison and iteration to find the optimal matching of ships. Compared with other matching methods, the algorithm proposed in this article is not affected by the moving background, which performs a better ship matching accuracy with a lower time cost.