Space Object Detection using Multi-frame Temporal Trajectory Completion Method

2025-10-22

AI Summary

To address high false-negative and false-positive rates in optical detection of geostationary orbit (GEO) space objects—caused by extremely weak signal intensity, complex stellar backgrounds, and strong environmental interference—this paper proposes a multi-frame temporal trajectory completion framework. The method innovatively employs wavelet transform to enhance high-frequency target features, designs a Hungarian-algorithm-based global optimal cross-frame association mechanism, and integrates temporal consistency filtering with progressive trajectory refinement for robust trajectory reconstruction. Evaluated on the SpotGEO dataset, the framework achieves an F₁-score of 90.14%, substantially outperforming state-of-the-art approaches. To the best of our knowledge, this is the first work to systematically integrate wavelet-based feature enhancement, global association optimization, and dynamic trajectory refinement, thereby significantly improving detection accuracy, trajectory continuity, and resilience to noise and clutter in GEO optical surveillance.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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%.

Problem

• Detecting faint GEO objects in noisy optical imaging data
• Matching space objects across frames using trajectory completion
• Reducing false and missing detections in multi-frame space observation

Innovation

• Wavelet transform enhances target features and suppresses noise
• Hungarian algorithm enables optimal multi-frame trajectory matching
• Post-processing pipeline filters noise and refines trajectories progressively