Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier

Petteri Nevavuori; Tarmo Lipping; Nathaniel Narra Girish; Petri Linna

doi:10.1109/IGARSS39084.2020.9323683

Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier

Petteri Nevavuori, Tarmo Lipping, Nathaniel Narra Girish, Petri Linna

Computing Sciences

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

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Abstract

In this paper, a novel cloud coverage assessment method for the Sentinel-2 data is presented. The method is based on the Random Forest classifier and the target values used in the training process are obtained by comparing the NDVI indexes calculated from the satellite and the UAV data. The developed method is shown to outperform the Sentinel Cloud Probability Mask (CLDPRB) and Scene Classification (SCL) data layers in detecting cloudy areas.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	IEEE
Pages	4661-4664
Number of pages	4
ISBN (Electronic)	978-1-7281-6374-1
ISBN (Print)	978-1-7281-6375-8
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9323683
Publication status	Published - 2020
Publication type	A4 Article in conference proceedings
Event	IEEE International Geoscience and Remote Sensing Symposium - Duration: 26 Sept 2020 → 2 Oct 2020

Publication series

Name	IEEE International Geoscience and Remote Sensing Symposium proceedings
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium
Period	26/09/20 → 2/10/20

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10.1109/IGARSS39084.2020.9323683

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@inproceedings{4e44b5e1386c47f2a74d1a1f02e2c680,

title = "Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier",

abstract = "In this paper, a novel cloud coverage assessment method for the Sentinel-2 data is presented. The method is based on the Random Forest classifier and the target values used in the training process are obtained by comparing the NDVI indexes calculated from the satellite and the UAV data. The developed method is shown to outperform the Sentinel Cloud Probability Mask (CLDPRB) and Scene Classification (SCL) data layers in detecting cloudy areas.",

author = "Petteri Nevavuori and Tarmo Lipping and {Narra Girish}, Nathaniel and Petri Linna",

note = "JUFOID=72039; IEEE International Geoscience and Remote Sensing Symposium ; Conference date: 26-09-2020 Through 02-10-2020",

year = "2020",

doi = "10.1109/IGARSS39084.2020.9323683",

language = "English",

isbn = "978-1-7281-6375-8",

series = "IEEE International Geoscience and Remote Sensing Symposium proceedings",

publisher = "IEEE",

pages = "4661--4664",

booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)",

address = "United States",

}

Nevavuori, P, Lipping, T, Narra Girish, N & Linna, P 2020, Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier. in 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium proceedings, IEEE, pp. 4661-4664, IEEE International Geoscience and Remote Sensing Symposium, 26/09/20. https://doi.org/10.1109/IGARSS39084.2020.9323683

Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier. / Nevavuori, Petteri; Lipping, Tarmo; Narra Girish, Nathaniel et al.
2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2020. p. 4661-4664 (IEEE International Geoscience and Remote Sensing Symposium proceedings).

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

TY - GEN

T1 - Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier

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AU - Narra Girish, Nathaniel

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PY - 2020

Y1 - 2020

N2 - In this paper, a novel cloud coverage assessment method for the Sentinel-2 data is presented. The method is based on the Random Forest classifier and the target values used in the training process are obtained by comparing the NDVI indexes calculated from the satellite and the UAV data. The developed method is shown to outperform the Sentinel Cloud Probability Mask (CLDPRB) and Scene Classification (SCL) data layers in detecting cloudy areas.

AB - In this paper, a novel cloud coverage assessment method for the Sentinel-2 data is presented. The method is based on the Random Forest classifier and the target values used in the training process are obtained by comparing the NDVI indexes calculated from the satellite and the UAV data. The developed method is shown to outperform the Sentinel Cloud Probability Mask (CLDPRB) and Scene Classification (SCL) data layers in detecting cloudy areas.

U2 - 10.1109/IGARSS39084.2020.9323683

DO - 10.1109/IGARSS39084.2020.9323683

M3 - Conference contribution

SN - 978-1-7281-6375-8

T3 - IEEE International Geoscience and Remote Sensing Symposium proceedings

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BT - 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Y2 - 26 September 2020 through 2 October 2020

ER -

Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier

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