Space Object Detection using Multi-frame Temporal Trajectory Completion Method
This addresses detection challenges for space objects in GEO, which is important for space surveillance and safety, but it appears incremental as it builds on existing techniques like wavelet transform and the Hungarian algorithm.
The paper tackled the problem of detecting space objects in Geostationary Earth Orbit (GEO) by enhancing high-frequency features and using a multi-frame temporal trajectory completion method, achieving an F1 score of 90.14% on the SpotGEO dataset.
Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this, we propose a multi-frame temporal trajectory completion scheme centered on the Hungarian algorithm for globally optimal cross-frame matching. To effectively mitigate missing and false detections, a series of key steps including temporal matching and interpolation completion, temporal-consistency-based noise filtering, and progressive trajectory refinement are designed in the post-processing pipeline. Experimental results on the public SpotGEO dataset demonstrate the effectiveness of the proposed method, achieving an F_1 score of 90.14%.