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CubeSat cloud detection based on JPEG2000 compression and deep learning

Zhang, Z., Xu, G. and Song, J. ORCID: 0000-0003-0623-0395 (2018). CubeSat cloud detection based on JPEG2000 compression and deep learning. Advances in Mechanical Engineering, 10(10), doi: 10.1177/1687814018808178

Abstract

In order to enhance the efficiency of the image transmission system and the robustness of the optical imaging system of the Association of Sino-Russian Technical Universities satellite, a new framework of on-board cloud detection by utilizing a lightweight U-Net and JPEG compression strategy is described. In this method, a careful compression strategy is introduced and evaluated to acquire a balanced result between the efficiency and power consuming. A deep-learning network combined with lightweight U-Net and Mobilenet is trained and verified with a public Landsat-8 data set Spatial Procedures for Automated Removal of Cloud and Shadow. Experiment results indicate that by utilizing image-compression strategy and depthwise separable convolutions, the maximum memory cost and inference speed are dramatically reduced into 0.7133 Mb and 0.0378 s per million pixels while the overall accuracy achieves around 93.1%. A good possibility of the on-board cloud detection based on deep learning is explored by the proposed method.

Publication Type: Article
Additional Information: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). This article has been published in Advances in Mechanical Engineering, doi: 10.1177/1687814018808178
Publisher Keywords: Deep learning, cloud detection, JPEG2000, CubeSat, ASRTU mission
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
Date available in CRO: 07 Dec 2021 11:20
Date of acceptance: 10 September 2018
Date of first online publication: 29 October 2018
URI: https://openaccess.city.ac.uk/id/eprint/27162
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