Quantifying trade-offs in satellite hardware configurations using a super-resolution framework with realistic image degradation-Reference-Cited by-同舟云学术

Quantifying trade-offs in satellite hardware configurations using a super-resolution framework with realistic image degradation

Published:2024-02-26 Issue:3 Volume:15 Page:291-301
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

White Jack¹,Codoreanu Alex¹²,Zuleta Ignacio³,Lynch Colm³,Marchisio Giovanni³,Qin Kai¹,Petrie Stephen M.¹⁴,Duffy Alan R.¹^ORCID

Affiliation:

1. Space Technology and Industry Institute, Swinburne University of Technology, Hawthorn, VIC, Austrialia

2. Gravitational Wave Data Centre, Swinburne University of Technology, Hawthorn, VIC, Austrialia

3. Payload Group, Planet Labs Inc, San Francisco, CA, USA

4. Centre for Transformative Innovation, Swinburne University of Technology, Hawthorn, VIC, Austrialia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2024.2318756

Reference20 articles.

1. Arefin, R., M. Vincent Michalski, P.-L. St-Charles, A. Kalaitzis, S. Kim, S. E. Kahou, and Y. Bengio. 2020. “Multi-Image Super-Resolution for Remote Sensing Using Deep Recurrent Networks.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, Washington, USA, 206–207.

2. Bell-Kligler, S., S. Assaf, and I. Michal. 2019. “Blind Super-Resolution Kernel Estimation Using an Internal-GAN”. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, Vol. 32.

3. Multidisciplinary design optimization for a high-resolution earth-imaging constellation

4. Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution