SiamS²F: Satellite Video Single-Object Tracking Based on a Siamese Spectral-Spatial-Frame Correlation Network

In recent years, satellite video object tracking has received widespread attention as a research hotspot, but is also faced with many challenges. In satellite video, moving objects usually consist of only a few pixels, which makes it more difficult for the tracker to distinguish the target from the background. Furthermore, occlusion factors such as clouds, trees, and bridges can bring challenges when relocating the target from adjacent frames. In this article, to solve the above-mentioned problems, we propose a Siamese spectral-spatial-frame (SiamS2F) correlation network, which combines deep spectral, spatial, and frame features to enhance the interaction between video frames, to better focus on the target continuity. First, the deep features of the video are extracted with a Siamese backbone, along with a channel-spatial attention module. Second, a spectral-spatial-frame (SSF) module is proposed, where the output features of the backbone are fused based on the graph attention layer, and then the intraframe and interframe information of the fused features is enhanced by a newly designed multiframe interactive attention (MFIA) mechanism. Third, to solve the problem of similar objects, an additional center loss function is proposed in the classification regression head (CRH), where the search area is limited to a local range by adding an inbox identifier, to reduce the impact of the similar objects around the target. The proposed method was tested on two challenging benchmark remote sensing datasets—SatSOT and SV248S—where SiamS2F outperformed the related state-of-the-art trackers.