Reference : Image Enhancement for Space Surveillance and Tracking
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/54459
Image Enhancement for Space Surveillance and Tracking
English
Jamrozik, Michele Lynn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Gaudilliere, Vincent mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Mohamed Ali, Mohamed Adel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
2022
Proceedings of the 73rd International Astronautical Congress
Jamrozik, Michele Lynn mailto
Gaudilliere, Vincent mailto
Musallam, Mohamed Adel mailto
Aouada, Djamila mailto
International Astronautical Federation
No
No
International
Paris
France
International Astronautical Congress
from 18-09-22 to 22-09-22
International Astronautical Federation
Paris
France
[en] Space Debris ; Image enhancement ; Deep Learning ; Object tracking ; Space ; Artificial Intelligence ; IAC-22 ; A6 ; x71536
[en] 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
University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >
Luxembourg National Research Fund
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/54459
FnR ; FNR14755859 > Djamila Aouada > MEET-A > Multi-modal Fusion Of Electro-optical Sensors For Spacecraft Pose Estimation Towards Autonomous In-orbit Operations > 01/01/2021 > 31/12/2023 > 2020

There is no file associated with this reference.

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.