Snow and cloud discrimination using convolutional neural networks

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

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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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.

Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Subtitle of host publicationISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”
Place of PublicationDehradun
Pages59-63
Number of pages5
Volume IV-5
DOIs
Publication statusPublished - 15 Nov 2018
Event2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People - Dehradun, India
Duration: 20 Nov 201823 Nov 2018
http://isprstc5india2018.org/

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)2194-9042

Conference

Conference2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People
CountryIndia
CityDehradun
Period20/11/1823/11/18
Internet address

Fingerprint

snow
Snow
discrimination
Neural networks
Masks
remote sensing
Remote sensing
masks
Satellites
Infrared radiation
metadata
cloud cover
Planets
Metadata
climate
planets
pixel
planet
Pixels
pixels

Keywords

  • ITC-HYBRID

Cite this

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). Dehradun. https://doi.org/10.5194/isprs-annals-IV-5-59-2018
Varshney, D. ; Gupta, P. K. ; Persello, C. ; Nikam, B. R. / 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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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.; Nikam, B. R.

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 proceedingChapterAcademicpeer-review

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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

UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2018/chap/persello_sno.pdf

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

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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). https://doi.org/10.5194/isprs-annals-IV-5-59-2018