Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data

Debvrat Varshney; Prasun Kumar Gupta; Claudio Persello; Bhaskar Ramachandra Nikam

doi:10.1109/IGARSS.2019.8898628

Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data

Debvrat Varshney, Prasun Kumar Gupta, Claudio Persello, Bhaskar Ramachandra Nikam

GeoAI

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

2 Scopus citations

Abstract

Cloud cover creates obstruction in Earth Observation studies. The obstruction is harder to distinguish from features having similar reflectance on the ground, such as snow. To distinguish clouds from snow in a VNIR image, we use an additional SWIR band. The images were fed into a deep Fully Convolutional Network that can fuse the multiresolution SWIR and VNIR bands together, in order to produce pixelwise classification. The accuracy obtained by the model on the test image was 93.35%. We compare the performance of this model with a more commonly used technique, Random Forests. To analyze the effect of SWIR, we use another deep learning model, trained only on the VNIR image, and compare the accuracies obtained.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	9851-9854
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8898628
State	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: Jul 28 2019 → Aug 2 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	07/28/19 → 08/2/19

Keywords

Cloud detection
Data fusion
Deep convolutional networks
LISS-4
SWIR
Snow

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10.1109/IGARSS.2019.8898628

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Varshney, D., Gupta, P. K., Persello, C., & Nikam, B. R. (2019). Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 9851-9854). Article 8898628 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8898628

Varshney, Debvrat ; Gupta, Prasun Kumar ; Persello, Claudio et al. / Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data. 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 9851-9854 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{f3d8097d110e48f7b4bfd59e5cead6f2,

title = "Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data",

abstract = "Cloud cover creates obstruction in Earth Observation studies. The obstruction is harder to distinguish from features having similar reflectance on the ground, such as snow. To distinguish clouds from snow in a VNIR image, we use an additional SWIR band. The images were fed into a deep Fully Convolutional Network that can fuse the multiresolution SWIR and VNIR bands together, in order to produce pixelwise classification. The accuracy obtained by the model on the test image was 93.35%. We compare the performance of this model with a more commonly used technique, Random Forests. To analyze the effect of SWIR, we use another deep learning model, trained only on the VNIR image, and compare the accuracies obtained.",

keywords = "Cloud detection, Data fusion, Deep convolutional networks, LISS-4, SWIR, Snow",

author = "Debvrat Varshney and Gupta, {Prasun Kumar} and Claudio Persello and Nikam, {Bhaskar Ramachandra}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

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series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Varshney, D, Gupta, PK, Persello, C & Nikam, BR 2019, Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8898628, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 9851-9854, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 07/28/19. https://doi.org/10.1109/IGARSS.2019.8898628

Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data. / Varshney, Debvrat; Gupta, Prasun Kumar; Persello, Claudio et al.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 9851-9854 8898628 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

TY - GEN

T1 - Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data

AU - Varshney, Debvrat

AU - Gupta, Prasun Kumar

AU - Persello, Claudio

AU - Nikam, Bhaskar Ramachandra

PY - 2019/7

Y1 - 2019/7

N2 - Cloud cover creates obstruction in Earth Observation studies. The obstruction is harder to distinguish from features having similar reflectance on the ground, such as snow. To distinguish clouds from snow in a VNIR image, we use an additional SWIR band. The images were fed into a deep Fully Convolutional Network that can fuse the multiresolution SWIR and VNIR bands together, in order to produce pixelwise classification. The accuracy obtained by the model on the test image was 93.35%. We compare the performance of this model with a more commonly used technique, Random Forests. To analyze the effect of SWIR, we use another deep learning model, trained only on the VNIR image, and compare the accuracies obtained.

AB - Cloud cover creates obstruction in Earth Observation studies. The obstruction is harder to distinguish from features having similar reflectance on the ground, such as snow. To distinguish clouds from snow in a VNIR image, we use an additional SWIR band. The images were fed into a deep Fully Convolutional Network that can fuse the multiresolution SWIR and VNIR bands together, in order to produce pixelwise classification. The accuracy obtained by the model on the test image was 93.35%. We compare the performance of this model with a more commonly used technique, Random Forests. To analyze the effect of SWIR, we use another deep learning model, trained only on the VNIR image, and compare the accuracies obtained.

KW - Cloud detection

KW - Data fusion

KW - Deep convolutional networks

KW - LISS-4

KW - SWIR

KW - Snow

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U2 - 10.1109/IGARSS.2019.8898628

DO - 10.1109/IGARSS.2019.8898628

M3 - Conference contribution

AN - SCOPUS:85077703256

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 9851

EP - 9854

BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

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Varshney D, Gupta PK, Persello C, Nikam BR. Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 9851-9854. 8898628. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8898628

Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data

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