MULS-Net: A Multilevel Supervised Network for Ship Tracking From Low-Resolution Remote-Sensing Image Sequences
Yuan Li, Qizhi Xu, Ziyang Kong, Wei Li · IEEE Transactions on Geoscience and Remote Sensing · 2023
Ship detection and tracking from remote sensing image sequences has become an increasingly important research point. However, there are still many challenges for ship tracking from low-resolution remote sensing image sequences: 1) the dim and small objects contain only a few shape and texture features, making it difficult to detect and track ships; 2) broken clouds often resemble ships, resulting in false tracking; 3) the ship may be occluded by clouds leading to missed tracking. To address these challenges, we proposed a novel multi-level supervision network for ship tracking from low-resolution remote sensing image sequences. First, we designed a gradient difference-guided object clarification network component to significantly improve the object saliency, which is also implemented based on the multi-frame correlation enhancement images to improve the feature strength of small targets in the input data. Second, to reduce the difficulty of completing complex tasks, a multi-level supervised network framework with multiple components was presented to achieve improving the target clarity, detecting targets and tracking targets step-by-step. Finally, to improve the trajectory integrity and tracking accuracy, a joint tracking method based on a low frame rate tracking criterion was proposed to control the state of target tracking module. The method was validated on a self-assembled dataset from the GaoFen-4 satellite. The experiment results show the stronger competitive and accuracy of the proposed method than other state-of-the-art object tracker.