A Deep Network Approach to Multitemporal Cloud Detection

Devis Tuia, Benjamin Kellenberger, Adrian Perez-suey, Gustau Camps-valls

Research output: Chapter in Book/Report/Conference proceedingConference paper

2 Citations (Scopus)

Abstract

We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.
Original languageEnglish
Title of host publication2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings
Subtitle of host publicationObserving, Understanding And Forecasting The Dynamics Of Our Planet
PublisherIEEE Xplore
Pages4351-4354
ISBN (Electronic)9781538671504, 9781538671498
ISBN (Print)9781538671511
DOIs
Publication statusPublished - 5 Nov 2018
EventIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Conference

ConferenceIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
CountrySpain
CityValencia
Period22/07/1827/07/18

Fingerprint

Meteosat
pixel
learning
time series
detection

Cite this

Tuia, D., Kellenberger, B., Perez-suey, A., & Camps-valls, G. (2018). A Deep Network Approach to Multitemporal Cloud Detection. In 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet (pp. 4351-4354). IEEE Xplore. https://doi.org/10.1109/IGARSS.2018.8517312
Tuia, Devis ; Kellenberger, Benjamin ; Perez-suey, Adrian ; Camps-valls, Gustau. / A Deep Network Approach to Multitemporal Cloud Detection. 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, 2018. pp. 4351-4354
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title = "A Deep Network Approach to Multitemporal Cloud Detection",
abstract = "We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.",
author = "Devis Tuia and Benjamin Kellenberger and Adrian Perez-suey and Gustau Camps-valls",
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Tuia, D, Kellenberger, B, Perez-suey, A & Camps-valls, G 2018, A Deep Network Approach to Multitemporal Cloud Detection. in 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, pp. 4351-4354, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8517312

A Deep Network Approach to Multitemporal Cloud Detection. / Tuia, Devis; Kellenberger, Benjamin; Perez-suey, Adrian; Camps-valls, Gustau.

2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, 2018. p. 4351-4354.

Research output: Chapter in Book/Report/Conference proceedingConference paper

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AB - We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.

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Tuia D, Kellenberger B, Perez-suey A, Camps-valls G. A Deep Network Approach to Multitemporal Cloud Detection. In 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore. 2018. p. 4351-4354 https://doi.org/10.1109/IGARSS.2018.8517312