Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery

Yutong Ding; Xiuqing Hu; Yuqing He; Mingqi Liu; Saijie Wang

doi:10.1007/978-3-030-60633-6_51

Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery

Yutong Ding, Xiuqing Hu, Yuqing He^*, Mingqi Liu, Saijie Wang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Cloud detection plays a very important role in the development of satellite remote sensing products and influences the accuracy of satellite products that characterize the properties of clouds, aerosols, trace gases, and ground surface parameters. However, current existing cloud detection methods rely heavily on the data of visible bands. It makes FY3D MERSI, which lacks visible band data at night, difficult to use these methods with high accuracy. In this paper, we proposed a cloud detection method based on deep learning termed CM-CNN for FY-3D MERSI. In order to ensure the effect of the network, the data has been strictly selected and consequently preprocessed. The method can automatically extract identified target features and fuse multi-level feature information, and adjust the parameters in the network without setting a threshold. Besides, this method proves to be better and more robust while only using mid-infrared and long-infrared band data in different cases.

Original language	English
Title of host publication	Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings
Editors	Yuxin Peng, Hongbin Zha, Qingshan Liu, Huchuan Lu, Zhenan Sun, Chenglin Liu, Xilin Chen, Jian Yang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	615-625
Number of pages	11
ISBN (Print)	9783030606329
DOIs	https://doi.org/10.1007/978-3-030-60633-6_51
Publication status	Published - 2020
Externally published	Yes
Event	3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020 - Nanjing, China Duration: 16 Oct 2020 → 18 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12305 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020
Country/Territory	China
City	Nanjing
Period	16/10/20 → 18/10/20

Keywords

CM-CNN
Cloud detection
Deep learning
FY-3D/MERSI
Remote sensing

Access to Document

10.1007/978-3-030-60633-6_51

Cite this

Ding, Y., Hu, X., He, Y., Liu, M., & Wang, S. (2020). Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery. In Y. Peng, H. Zha, Q. Liu, H. Lu, Z. Sun, C. Liu, X. Chen, & J. Yang (Eds.), Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings (pp. 615-625). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12305 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60633-6_51

Ding, Yutong ; Hu, Xiuqing ; He, Yuqing et al. / Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery. Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. editor / Yuxin Peng ; Hongbin Zha ; Qingshan Liu ; Huchuan Lu ; Zhenan Sun ; Chenglin Liu ; Xilin Chen ; Jian Yang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 615-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f1fb3201ff994f4184426344255f4040,

title = "Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery",

abstract = "Cloud detection plays a very important role in the development of satellite remote sensing products and influences the accuracy of satellite products that characterize the properties of clouds, aerosols, trace gases, and ground surface parameters. However, current existing cloud detection methods rely heavily on the data of visible bands. It makes FY3D MERSI, which lacks visible band data at night, difficult to use these methods with high accuracy. In this paper, we proposed a cloud detection method based on deep learning termed CM-CNN for FY-3D MERSI. In order to ensure the effect of the network, the data has been strictly selected and consequently preprocessed. The method can automatically extract identified target features and fuse multi-level feature information, and adjust the parameters in the network without setting a threshold. Besides, this method proves to be better and more robust while only using mid-infrared and long-infrared band data in different cases.",

keywords = "CM-CNN, Cloud detection, Deep learning, FY-3D/MERSI, Remote sensing",

author = "Yutong Ding and Xiuqing Hu and Yuqing He and Mingqi Liu and Saijie Wang",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020 ; Conference date: 16-10-2020 Through 18-10-2020",

year = "2020",

doi = "10.1007/978-3-030-60633-6_51",

language = "English",

isbn = "9783030606329",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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

pages = "615--625",

editor = "Yuxin Peng and Hongbin Zha and Qingshan Liu and Huchuan Lu and Zhenan Sun and Chenglin Liu and Xilin Chen and Jian Yang",

booktitle = "Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings",

address = "Germany",

}

Ding, Y, Hu, X, He, Y, Liu, M & Wang, S 2020, Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery. in Y Peng, H Zha, Q Liu, H Lu, Z Sun, C Liu, X Chen & J Yang (eds), Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12305 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 615-625, 3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020, Nanjing, China, 16/10/20. https://doi.org/10.1007/978-3-030-60633-6_51

Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery. / Ding, Yutong; Hu, Xiuqing; He, Yuqing et al.
Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. ed. / Yuxin Peng; Hongbin Zha; Qingshan Liu; Huchuan Lu; Zhenan Sun; Chenglin Liu; Xilin Chen; Jian Yang. Springer Science and Business Media Deutschland GmbH, 2020. p. 615-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12305 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery

AU - Ding, Yutong

AU - Hu, Xiuqing

AU - He, Yuqing

AU - Liu, Mingqi

AU - Wang, Saijie

PY - 2020

Y1 - 2020

N2 - Cloud detection plays a very important role in the development of satellite remote sensing products and influences the accuracy of satellite products that characterize the properties of clouds, aerosols, trace gases, and ground surface parameters. However, current existing cloud detection methods rely heavily on the data of visible bands. It makes FY3D MERSI, which lacks visible band data at night, difficult to use these methods with high accuracy. In this paper, we proposed a cloud detection method based on deep learning termed CM-CNN for FY-3D MERSI. In order to ensure the effect of the network, the data has been strictly selected and consequently preprocessed. The method can automatically extract identified target features and fuse multi-level feature information, and adjust the parameters in the network without setting a threshold. Besides, this method proves to be better and more robust while only using mid-infrared and long-infrared band data in different cases.

AB - Cloud detection plays a very important role in the development of satellite remote sensing products and influences the accuracy of satellite products that characterize the properties of clouds, aerosols, trace gases, and ground surface parameters. However, current existing cloud detection methods rely heavily on the data of visible bands. It makes FY3D MERSI, which lacks visible band data at night, difficult to use these methods with high accuracy. In this paper, we proposed a cloud detection method based on deep learning termed CM-CNN for FY-3D MERSI. In order to ensure the effect of the network, the data has been strictly selected and consequently preprocessed. The method can automatically extract identified target features and fuse multi-level feature information, and adjust the parameters in the network without setting a threshold. Besides, this method proves to be better and more robust while only using mid-infrared and long-infrared band data in different cases.

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KW - Cloud detection

KW - Deep learning

KW - FY-3D/MERSI

KW - Remote sensing

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U2 - 10.1007/978-3-030-60633-6_51

DO - 10.1007/978-3-030-60633-6_51

M3 - Conference contribution

AN - SCOPUS:85093857306

SN - 9783030606329

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 615

EP - 625

BT - Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings

A2 - Peng, Yuxin

A2 - Zha, Hongbin

A2 - Liu, Qingshan

A2 - Lu, Huchuan

A2 - Sun, Zhenan

A2 - Liu, Chenglin

A2 - Chen, Xilin

A2 - Yang, Jian

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020

Y2 - 16 October 2020 through 18 October 2020

ER -

Ding Y, Hu X, He Y, Liu M, Wang S. Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery. In Peng Y, Zha H, Liu Q, Lu H, Sun Z, Liu C, Chen X, Yang J, editors, Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 615-625. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60633-6_51

Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery

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