Enhancing Satellite Trail Detection in Night Sky Imagery with Automatic Salience Thresholding

This paper proposes a novel automatic thresholding method called Automatic Salience Thresholding (AST) for creating binary masks for detecting and removing satellite streaks in night sky imagery. Our approach utilizes a combination of Gaussian filtering, a salience-based thresholding technique, morphological filtering and line detection using Probabilistic Hough Transformations to identify the satellite trail in the image. We evaluated our method on diverse datasets of night sky images containing satellite trails in varying lighting conditions. Our results show that AST outperforms the compared methods regarding accuracy and speed. Our proposed AST method provides a reliable and efficient solution for detecting satellite trails while balancing selectivity and sensitivity.