Faint Space Linear Streak Detection via Multidirectional Thresholding and Geometric Clustering

Abstract

The detection of streaking space objects in wide-field space observation images is essential for space environment awareness, as these images serve as the core data for target cataloging and collision risk assessment. However, this task is fundamentally challenged by the elongated and arbitrarily oriented morphology of space streaks and their typically low signal-to-noise ratio. These challenges often lead to fragmented detections, posing a significant obstacle for accurate cataloging and tracking. To address these issues, this work introduces two key innovations. First, we propose a local adaptive thresholding method for streak segmentation that estimates thresholds along multiple directions and employs the median absolute deviation to eliminate outliers, thereby enhancing segmentation accuracy for streaks of arbitrary orientations in cluttered backgrounds. Second, we develop an enhanced density-based spatial clustering algorithm (DBSCAN) with geometric constraints for streak labeling. This method bridges gaps in fragmented detections and reconstructs discontinuous structures, addressing the limitations of traditional 8-connected component labeling. By explicitly modeling the streak geometry, the proposed DBSCAN variant integrates adaptive geometric constraints into cluster labeling, with key enhancements—including recursive line fitting, adaptive neighborhood radius adjustment, and density-weighted clustering—to achieve robust target identification within complex point sets. The experimental evaluation demonstrates that the multidirectional thresholding and geometry-constrained clustering components of the proposed framework provide enhanced capability for detecting faint streaks and suppressing false alarms.