The Spatiotemporal and Frequency-Domain Learning Framework for Moving Object Detection in Satellite Video

Moving object detection (MOD) in satellite video sequences faces persistent challenges, including low contrast against complex backgrounds, limited motion modeling, and severe scale variation. To effectively address these, we propose STFDNet, a novel combined model- and data-driven framework. STFDNet comprises three core components: the hybrid temporal motion module (HTMM), the dynamic frequency learning (DFL), and a progressive cascaded learning strategy (PCLS). Initially, HTMM leverages both explicit and implicit strategies to model multiframe temporal differences, effectively capturing both short- and long-term motions. Then, DFL integrates a frequency-domain dynamic filter to learn global frequency characteristics, enhancing feature representation beyond the spatial domain and improving distinction from background noise. Finally, PCLS progressively refines detection results by fusing multidomain features in a step-by-step manner, transitioning from coarse- to fine-grained representations. Experiments on the Jilin-1 satellite video dataset demonstrate that the proposed method effectively mitigates background noise, limited motion modeling, and object scale variations, significantly improving detection accuracy and robustness, achieving an $F1$ -score of 85.4%.