Data Transformation for Super-Resolution on Ocean Remote Sensing Images

Yuting Yang; Kin Man Lam; Xin Sun; Junyu Dong; Muwei Jian; Hanjiang Luo

doi:10.1007/978-3-031-03948-5_35

Data Transformation for Super-Resolution on Ocean Remote Sensing Images

Yuting Yang, Kin Man Lam, Xin Sun, Junyu Dong, Muwei Jian, Hanjiang Luo

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

High-resolution ocean remote sensing imaging is of vital importance for research in the field of ocean remote sensing. However, the available ocean remote sensing images are often averaged data, whose resolution is lower than the instant remote sensing images. In this paper, we propose a data transformation method to process remote sensing images in different locations and resolutions. We target satellite-derived sea surface temperature (SST) images as a specific case-study. In detail, we use a modified very deep super-resolution (VDSR) model as our baseline model and propose a data transformation method to improve the robustness of the model. Furthermore, we also illustrates how the degree of difference in the data distribution influences the model’s robustness and also, how our proposed data transformation method can improve the model’s robustness. Experiment results prove that our method is effective and our model is robust.

Original language	English
Title of host publication	Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings
Editors	Zhongzhi Shi, Jean-Daniel Zucker, Bo An
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	431-443
Number of pages	13
Volume	643
ISBN (Print)	9783031039478
DOIs	https://doi.org/10.1007/978-3-031-03948-5_35
Publication status	Published - May 2022
Event	12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022 - Qingdao, China Duration: 27 May 2022 → 30 May 2022

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	643 IFIP
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022
Country/Territory	China
City	Qingdao
Period	27/05/22 → 30/05/22

Keywords

Data transformation
Deep learning
Ocean-front
Sea surface temperature
Super-resolution

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications
Information Systems and Management

More information

10.1007/978-3-031-03948-5_35

Cite this

Yang, Y., Lam, K. M., Sun, X., Dong, J., Jian, M., & Luo, H. (2022). Data Transformation for Super-Resolution on Ocean Remote Sensing Images. In Z. Shi, J-D. Zucker, & B. An (Eds.), Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings (Vol. 643, pp. 431-443). (IFIP Advances in Information and Communication Technology; Vol. 643 IFIP). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-03948-5_35

Yang, Yuting ; Lam, Kin Man ; Sun, Xin et al. / Data Transformation for Super-Resolution on Ocean Remote Sensing Images. Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings. editor / Zhongzhi Shi ; Jean-Daniel Zucker ; Bo An. Vol. 643 Springer Science and Business Media Deutschland GmbH, 2022. pp. 431-443 (IFIP Advances in Information and Communication Technology).

@inproceedings{f588688ddc5a44028ce3d918b53c0115,

title = "Data Transformation for Super-Resolution on Ocean Remote Sensing Images",

abstract = "High-resolution ocean remote sensing imaging is of vital importance for research in the field of ocean remote sensing. However, the available ocean remote sensing images are often averaged data, whose resolution is lower than the instant remote sensing images. In this paper, we propose a data transformation method to process remote sensing images in different locations and resolutions. We target satellite-derived sea surface temperature (SST) images as a specific case-study. In detail, we use a modified very deep super-resolution (VDSR) model as our baseline model and propose a data transformation method to improve the robustness of the model. Furthermore, we also illustrates how the degree of difference in the data distribution influences the model{\textquoteright}s robustness and also, how our proposed data transformation method can improve the model{\textquoteright}s robustness. Experiment results prove that our method is effective and our model is robust.",

keywords = "Data transformation, Deep learning, Ocean-front, Sea surface temperature, Super-resolution",

author = "Yuting Yang and Lam, {Kin Man} and Xin Sun and Junyu Dong and Muwei Jian and Hanjiang Luo",

note = "Funding Information: Acknowledgments. This work was jointly supported by the National Natural Science Foundation of China (No. U1706218, 61971388, 62072287). Publisher Copyright: {\textcopyright} 2022, IFIP International Federation for Information Processing.; 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022 ; Conference date: 27-05-2022 Through 30-05-2022",

year = "2022",

month = may,

doi = "10.1007/978-3-031-03948-5_35",

language = "English",

isbn = "9783031039478",

volume = "643",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "431--443",

editor = "Zhongzhi Shi and Jean-Daniel Zucker and Bo An",

booktitle = "Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings",

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}

Yang, Y, Lam, KM, Sun, X, Dong, J, Jian, M & Luo, H 2022, Data Transformation for Super-Resolution on Ocean Remote Sensing Images. in Z Shi, J-D Zucker & B An (eds), Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings. vol. 643, IFIP Advances in Information and Communication Technology, vol. 643 IFIP, Springer Science and Business Media Deutschland GmbH, pp. 431-443, 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022, Qingdao, China, 27/05/22. https://doi.org/10.1007/978-3-031-03948-5_35

Data Transformation for Super-Resolution on Ocean Remote Sensing Images. / Yang, Yuting; Lam, Kin Man; Sun, Xin et al.
Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings. ed. / Zhongzhi Shi; Jean-Daniel Zucker; Bo An. Vol. 643 Springer Science and Business Media Deutschland GmbH, 2022. p. 431-443 (IFIP Advances in Information and Communication Technology; Vol. 643 IFIP).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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T1 - Data Transformation for Super-Resolution on Ocean Remote Sensing Images

AU - Yang, Yuting

AU - Lam, Kin Man

AU - Sun, Xin

AU - Dong, Junyu

AU - Jian, Muwei

AU - Luo, Hanjiang

N1 - Funding Information: Acknowledgments. This work was jointly supported by the National Natural Science Foundation of China (No. U1706218, 61971388, 62072287). Publisher Copyright: © 2022, IFIP International Federation for Information Processing.

PY - 2022/5

Y1 - 2022/5

N2 - High-resolution ocean remote sensing imaging is of vital importance for research in the field of ocean remote sensing. However, the available ocean remote sensing images are often averaged data, whose resolution is lower than the instant remote sensing images. In this paper, we propose a data transformation method to process remote sensing images in different locations and resolutions. We target satellite-derived sea surface temperature (SST) images as a specific case-study. In detail, we use a modified very deep super-resolution (VDSR) model as our baseline model and propose a data transformation method to improve the robustness of the model. Furthermore, we also illustrates how the degree of difference in the data distribution influences the model’s robustness and also, how our proposed data transformation method can improve the model’s robustness. Experiment results prove that our method is effective and our model is robust.

AB - High-resolution ocean remote sensing imaging is of vital importance for research in the field of ocean remote sensing. However, the available ocean remote sensing images are often averaged data, whose resolution is lower than the instant remote sensing images. In this paper, we propose a data transformation method to process remote sensing images in different locations and resolutions. We target satellite-derived sea surface temperature (SST) images as a specific case-study. In detail, we use a modified very deep super-resolution (VDSR) model as our baseline model and propose a data transformation method to improve the robustness of the model. Furthermore, we also illustrates how the degree of difference in the data distribution influences the model’s robustness and also, how our proposed data transformation method can improve the model’s robustness. Experiment results prove that our method is effective and our model is robust.

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KW - Deep learning

KW - Ocean-front

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KW - Super-resolution

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DO - 10.1007/978-3-031-03948-5_35

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SN - 9783031039478

VL - 643

T3 - IFIP Advances in Information and Communication Technology

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EP - 443

BT - Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings

A2 - Shi, Zhongzhi

A2 - Zucker, Jean-Daniel

A2 - An, Bo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022

Y2 - 27 May 2022 through 30 May 2022

ER -

Yang Y, Lam KM, Sun X, Dong J, Jian M, Luo H. Data Transformation for Super-Resolution on Ocean Remote Sensing Images. In Shi Z, Zucker J-D, An B, editors, Intelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings. Vol. 643. Springer Science and Business Media Deutschland GmbH. 2022. p. 431-443. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-031-03948-5_35

Data Transformation for Super-Resolution on Ocean Remote Sensing Images

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