A COMPUTER VISION APPROACH FOR DETECTION OF ASTEROIDS/COMETS IN SPACE SATELLITE IMAGES

Abstract. There are many satellites orbiting around the earth capturing huge amounts of images from astronomical objects and sending them to ground stations to be stored and analyzed. This results in an increasing demand for processing and analyzing images with autonomous algorithms. NEOSSat is one of the Canadian satellites that investigates the outer space to discover new comets/asteroids in our solar system. In this paper, we proposed a method based on computer vision techniques to detect the moving objects in NEOSSat images autonomously and also estimate their path. Our method is able to detect the comet/asteroids that are only %2 different in brightness with respect to the background. Moreover, it is not limited to the linear trajectory of the object and can detect objects following a curved path. This method does not depend on the length of the trajectory as well, detecting trajectories as short as 19 pixels. Our method is computationally efficient and can be run on a laptop. We also designed a graphical user interface for our software, encouraging public usage. The proposed software won the first place of the Canadian Space Agency’s Space Apps Challenge 2019 nationwide.