Snow and cloud discrimination using convolutional neural networks

D. Varshney; P.K. Gupta; C. Persello; B.R. Nikam

doi:10.5194/isprs-annals-IV-5-59-2018

Snow and cloud discrimination using convolutional neural networks

D. Varshney^*, P.K. Gupta, C. Persello, B.R. Nikam

^*Corresponding author for this work

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

4 Citations (Scopus)

105 Downloads (Pure)

Abstract

Snow is an important feature on our planet, and measuring its extent has advantages in climate studies. Snow mapping through satellite remote sensing is often affected by cloud cover. This issue can be resolved by using short wave infrared (SWIR) sensors. In order to obtain an effective cloud mask, our study aims to use SWIR data of a ResourceSat-2 satellite. We employ Convolutional Neural Networks (CNN) to discriminate similar pixels of clouds and snow. The technique is expected to give a high accuracy compared to traditional methods such as thresholding. The cloud mask thus produced can also be used for creating the metadata for Indian Remote Sensing products.

Original language	English
Title of host publication	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Subtitle of host publication	ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”
Place of Publication	Dehradun
Pages	59-63
Number of pages	5
Volume	IV-5
DOIs	https://doi.org/10.5194/isprs-annals-IV-5-59-2018
Publication status	Published - 15 Nov 2018
Event	2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People - Dehradun, India Duration: 20 Nov 2018 → 23 Nov 2018 http://isprstc5india2018.org/

Publication series

Name	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publisher	Copernicus
ISSN (Print)	2194-9042

Conference

Conference	2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People
Country/Territory	India
City	Dehradun
Period	20/11/18 → 23/11/18
Internet address	http://isprstc5india2018.org/

Keywords

ITC-HYBRID
Convolutional neural networks
SWIR
ReLU
Machine learning
Remote sensing

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Varshney2018snowFinal published version, 940 KBLicence: CC BY

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Varshney, D., Gupta, P. K., Persello, C., & Nikam, B. R. (2018). Snow and cloud discrimination using convolutional neural networks. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People” (Vol. IV-5, pp. 59-63). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences).. https://doi.org/10.5194/isprs-annals-IV-5-59-2018

Varshney, D. ; Gupta, P.K. ; Persello, C. et al. / Snow and cloud discrimination using convolutional neural networks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”. Vol. IV-5 Dehradun, 2018. pp. 59-63 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences).

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abstract = "Snow is an important feature on our planet, and measuring its extent has advantages in climate studies. Snow mapping through satellite remote sensing is often affected by cloud cover. This issue can be resolved by using short wave infrared (SWIR) sensors. In order to obtain an effective cloud mask, our study aims to use SWIR data of a ResourceSat-2 satellite. We employ Convolutional Neural Networks (CNN) to discriminate similar pixels of clouds and snow. The technique is expected to give a high accuracy compared to traditional methods such as thresholding. The cloud mask thus produced can also be used for creating the metadata for Indian Remote Sensing products.",

keywords = "ITC-HYBRID, Convolutional neural networks, SWIR, ReLU, Machine learning, Remote sensing",

author = "D. Varshney and P.K. Gupta and C. Persello and B.R. Nikam",

year = "2018",

month = nov,

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doi = "10.5194/isprs-annals-IV-5-59-2018",

language = "English",

volume = " IV-5",

series = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",

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booktitle = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",

note = "2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People ; Conference date: 20-11-2018 Through 23-11-2018",

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Varshney, D, Gupta, PK, Persello, C & Nikam, BR 2018, Snow and cloud discrimination using convolutional neural networks. in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”. vol. IV-5, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Dehradun, pp. 59-63, 2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People, Dehradun, India, 20/11/18. https://doi.org/10.5194/isprs-annals-IV-5-59-2018

Snow and cloud discrimination using convolutional neural networks. / Varshney, D.; Gupta, P.K.; Persello, C. et al.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”. Vol. IV-5 Dehradun, 2018. p. 59-63 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences).

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

TY - CHAP

T1 - Snow and cloud discrimination using convolutional neural networks

AU - Varshney, D.

AU - Gupta, P.K.

AU - Persello, C.

AU - Nikam, B.R.

PY - 2018/11/15

Y1 - 2018/11/15

N2 - Snow is an important feature on our planet, and measuring its extent has advantages in climate studies. Snow mapping through satellite remote sensing is often affected by cloud cover. This issue can be resolved by using short wave infrared (SWIR) sensors. In order to obtain an effective cloud mask, our study aims to use SWIR data of a ResourceSat-2 satellite. We employ Convolutional Neural Networks (CNN) to discriminate similar pixels of clouds and snow. The technique is expected to give a high accuracy compared to traditional methods such as thresholding. The cloud mask thus produced can also be used for creating the metadata for Indian Remote Sensing products.

AB - Snow is an important feature on our planet, and measuring its extent has advantages in climate studies. Snow mapping through satellite remote sensing is often affected by cloud cover. This issue can be resolved by using short wave infrared (SWIR) sensors. In order to obtain an effective cloud mask, our study aims to use SWIR data of a ResourceSat-2 satellite. We employ Convolutional Neural Networks (CNN) to discriminate similar pixels of clouds and snow. The technique is expected to give a high accuracy compared to traditional methods such as thresholding. The cloud mask thus produced can also be used for creating the metadata for Indian Remote Sensing products.

KW - ITC-HYBRID

KW - Convolutional neural networks

KW - SWIR

KW - ReLU

KW - Machine learning

KW - Remote sensing

U2 - 10.5194/isprs-annals-IV-5-59-2018

DO - 10.5194/isprs-annals-IV-5-59-2018

M3 - Chapter

AN - SCOPUS:85057631256

VL - IV-5

T3 - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SP - 59

EP - 63

BT - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

CY - Dehradun

T2 - 2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People

Y2 - 20 November 2018 through 23 November 2018

ER -

Varshney D, Gupta PK, Persello C, Nikam BR. Snow and cloud discrimination using convolutional neural networks. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”. Vol. IV-5. Dehradun. 2018. p. 59-63. (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). doi: 10.5194/isprs-annals-IV-5-59-2018

Snow and cloud discrimination using convolutional neural networks

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