Version 1
: Received: 15 February 2018 / Approved: 15 February 2018 / Online: 15 February 2018 (16:49:55 CET)
How to cite:
Zhaoxiang, Z.; Iwasaki, A.; Guodong, X.; Jianing, S. Small Satellite Cloud Detection Based On Deep Learning and Image Compression. Preprints2018, 2018020103. https://doi.org/10.20944/preprints201802.0103.v1
Zhaoxiang, Z.; Iwasaki, A.; Guodong, X.; Jianing, S. Small Satellite Cloud Detection Based On Deep Learning and Image Compression. Preprints 2018, 2018020103. https://doi.org/10.20944/preprints201802.0103.v1
Zhaoxiang, Z.; Iwasaki, A.; Guodong, X.; Jianing, S. Small Satellite Cloud Detection Based On Deep Learning and Image Compression. Preprints2018, 2018020103. https://doi.org/10.20944/preprints201802.0103.v1
APA Style
Zhaoxiang, Z., Iwasaki, A., Guodong, X., & Jianing, S. (2018). Small Satellite Cloud Detection Based On Deep Learning and Image Compression. Preprints. https://doi.org/10.20944/preprints201802.0103.v1
Chicago/Turabian Style
Zhaoxiang, Z., Xu Guodong and Song Jianing. 2018 "Small Satellite Cloud Detection Based On Deep Learning and Image Compression" Preprints. https://doi.org/10.20944/preprints201802.0103.v1
Abstract
An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.
Keywords
Cloud detection; Deep learning; Image Compression.
Subject
Environmental and Earth Sciences, Space and Planetary Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Oluwamuyiwa Adesola Adeegbe
The commenter has declared there is no conflict of interests.
I was thinking, do you have GitHub repository for your code i.e. the model, dataset e.t.c.