Space Debris: Are Deep Learning-based Image Enhancements part of the Solution?

in Proceedings International Symposium on Computational Sensing (2023)

The volume of space debris currently orbiting the Earth is reaching an unsustainable level at an accelerated pace. The detection, tracking, identification, and differentiation between orbit-defined ... [more ▼]

The volume of space debris currently orbiting the Earth is reaching an unsustainable level at an accelerated pace. The detection, tracking, identification, and differentiation between orbit-defined, registered spacecraft, and rogue/inactive space “objects”, is critical to asset protection. The primary objective of this work is to investigate the validity of Deep Neural Network (DNN) solutions to overcome the limitations and image artefacts most prevalent when captured with monocular cameras in the visible light spectrum. In this work, a hybrid UNet-ResNet34 Deep Learning (DL) architecture pre-trained on the ImageNet dataset, is developed. Image degradations addressed include blurring, exposure issues, poor contrast, and noise. The shortage of space-generated data suitable for supervised DL is also addressed. A visual comparison between the URes34P model developed in this work and the existing state of the art in deep learning image enhancement methods, relevant to images captured in space, is presented. Based upon visual inspection, it is determined that our UNet model is capable of correcting for space-related image degradations and merits further investigation to reduce its computational complexity. [less ▲]

Image Enhancement for Space Surveillance and Tracking

in Jamrozik, Michele Lynn; Gaudilliere, Vincent; Musallam, Mohamed Adel (Eds.) et al Proceedings of the 73rd International Astronautical Congress (2022)

Images generated in space with monocular camera payloads suffer degradations that hinder their utility in precision tracking applications including debris identification, removal, and in-orbit servicing ... [more ▼]

Images generated in space with monocular camera payloads suffer degradations that hinder their utility in precision tracking applications including debris identification, removal, and in-orbit servicing. To address the substandard quality of images captured in space and make them more reliable in space object tracking applications, several Image Enhancement (IE) techniques are investigated in this work. In addition, two novel space IE methods were developed. The first method called REVEAL, relies upon the application of more traditional image processing enhancement techniques and assumes a Retinex image formation model. A subsequent method, based on a UNet Deep Learning (DL) model was also developed. Image degradations addressed include blurring, exposure issues, poor contrast, and noise. The shortage of space-generated data suitable for supervised DL is also addressed. A visual comparison of both techniques developed was conducted and compared against the current state-of-the-art in DL-based IE methods relevant to images captured in space. It is determined in this work that both the REVEAL and the UNet-based DL solutions developed are well suited to correct for the degradations most often found in space images. In addition, it has been found that enhancing images in a pre-processing stage facilitates the subsequent extraction of object contours and metrics. By extracting information through image metrics, object properties such as size and orientation that enable more precise space object tracking may be more easily determined. Keywords: Deep Learning, Space, Image Enhancement, Space Debris [less ▲]

Deep Learning-based Image Enhancement for Space Applications

Presentation (2021, September)