Abstract

Detection, classification, and characterization of Space Objects (SOs) are important tasks in many areas of research. Detection of SOs is important in predicting collisions with debris that could become hazards for satellites or space missions in Near Earth orbits. This paper describes a flexible pipeline able to detect sunlit SOs automatically in images acquired using an all-sky camera with a large field of view (FoV). The proposed pipeline includes the following main steps: image distortion correction, filtering for noise reduction, generation of a background model for subtraction, star elimination using a star catalog, local-based contrast enhancement, and, finally, for automatic SO detection, two methodologies were developed to detect line segments. The first one uses a Canny edge detector and a Progressive Probabilistic Hough Transform, and the second is based on the Radon Transform for detecting line segments. The method was applied to a dataset of 22 x 3 images obtained from the Omnidirectional Space Situational Awareness (OmniSSA) Array at the Georgia Institute of Technology in downtown Atlanta. The OmniSSA array has 3 sensors that capture high-resolution images simultaneously (3352 x 2532 pixels) using a wide FoV for each camera. An intensity scaled by noise (I-SN) signal was defined and measured to show improvement in SO detection objectively. Fusing images from the three OmniSSA sensors after the background subtraction step improved both the I-SN and visualization during the detection stage. Ground-truth data were extracted from a Space-Track catalog and marked by human experts to validate the results of the pipeline, considering information from Astrometry.net. Results showed that almost all the SOs were correctly detected by the pipeline. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.