Autonomous Satellite Detection and Tracking using Optical Flow

TL;DR

This paper presents an autonomous optical flow-based method for detecting and tracking satellites in space images, distinguishing them from stars by their unique motion profiles, with validation on simulated and real imagery.

Contribution

Introduces a novel autonomous satellite detection algorithm using optical flow to differentiate moving objects from stars in space imagery.

Findings

Effective in simulated star images and ground-based satellite imagery.

Provides a comparative analysis of commercial and open-source software approaches.

Demonstrates accurate detection and tracking of resident space objects.

Abstract

In this paper, an autonomous method of satellite detection and tracking in images is implemented using optical flow. Optical flow is used to estimate the image velocities of detected objects in a series of space images. Given that most objects in an image will be stars, the overall image velocity from star motion is used to estimate the image's frame-to-frame motion. Objects seen to be moving with velocity profiles distinct from the overall image velocity are then classified as potential resident space objects. The detection algorithm is exercised using both simulated star images and ground-based imagery of satellites. Finally, this algorithm will be tested and compared using a commercial and an open-source software approach to provide the reader with two different options based on their need.