Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects

TL;DR

This paper presents a novel framework combining synthetic data generation, image restoration, and deep learning to improve pose estimation of space objects from blurred images, aiding collision avoidance and debris removal.

Contribution

It introduces an innovative synthetic dataset creation method and demonstrates effective use of U-Net and Resnet50 for image recovery and pose estimation of space objects.

Findings

U-Net-based image restoration reduced MSE by 97.28%.

Pose estimation error decreased by 71.9%.

Synthetic datasets enable effective training for real-world space object analysis.

Abstract

As the density of spacecraft in Earth's orbit increases, their recognition, pose and trajectory identification becomes crucial for averting potential collisions and executing debris removal operations. However, training models able to identify a spacecraft and its pose presents a significant challenge due to a lack of available image data for model training. This paper puts forth an innovative framework for generating realistic synthetic datasets of Resident Space Object (RSO) imagery. Using the International Space Station (ISS) as a test case, it goes on to combine image regression with image restoration methodologies to estimate pose from blurred images. An analysis of the proposed image recovery and regression techniques was undertaken, providing insights into the performance, potential enhancements and limitations when applied to real imagery of RSOs. The image recovery approach investigated involves first applying image deconvolution using an effective point spread function, followed by detail object extraction with a U-Net. Interestingly, using only U-Net for image reconstruction the best pose performance was attained, reducing the average Mean Squared Error in image recovery by 97.28% and the average angular error by 71.9%. The successful application of U-Net image restoration combined with the Resnet50 regression network for pose estimation of the International Space Station demonstrates the value of a diverse set of evaluation tools for effective solutions to real-world problems such as the analysis of distant objects in Earth's orbit.