6533b862fe1ef96bd12c641b
RESEARCH PRODUCT
A Deep Network Approach to Multitemporal Cloud Detection
Devis TuiaGustau Camps-vallsBenjamin KellenbergerAdrian Perez-sueysubject
FOS: Computer and information sciencesComputer Science - Machine Learning010504 meteorology & atmospheric sciencesComputer scienceFeature extraction0211 other engineering and technologiesCloud detectionFOS: Physical sciencesCloud computing02 engineering and technologyCloud detection01 natural sciencesMachine Learning (cs.LG)Laboratory of Geo-information Science and Remote SensingLaboratorium voor Geo-informatiekunde en Remote Sensing021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingbusiness.industrySeviriDeep learningDeep learningPE&RCPhysics - Atmospheric and Oceanic PhysicsRecurrent neural networkRecurrent neural networksAtmospheric and Oceanic Physics (physics.ao-ph)Convolutional neural networksSatelliteArtificial intelligencebusinessNetwork approachdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 | IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium |