A Deep Network Approach to Multitemporal Cloud Detection
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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| Main Authors: | , , , |
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| Format: | Article in monograph or in proceedings biblioteca |
| Language: | English |
| Published: |
IEEE
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| Subjects: | Cloud detection, Convolutional neural networks, Deep learning, Recurrent neural networks, Seviri, |
| Online Access: | https://research.wur.nl/en/publications/a-deep-network-approach-to-multitemporal-cloud-detection |
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| Summary: | 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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