Space Object Detection using Multi-frame Temporal Trajectory Completion Method

TL;DR

This paper introduces a novel multi-frame trajectory completion method for detecting space objects in GEO, combining wavelet transform and Hungarian algorithm to improve detection accuracy amidst challenging backgrounds.

Contribution

It presents a new multi-frame trajectory completion scheme with a global optimal matching approach and a comprehensive post-processing pipeline for space object detection.

Findings

Achieved an F_1 score of 90.14% on SpotGEO dataset.

Effectively suppresses background noise and enhances GEO target features.

Improves detection robustness in complex space environments.

Abstract

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%.