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A deep network approach to multitemporal cloud detection

2020/12/09 by Devis Tuia, Benjamin Kellenberger, Tuia, Devis +5
Engineering · Environmental Science · #Advanced Image Fusion Techniques #Atmospheric and Oceanic Physics (physics.ao-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Remote Sensing in Agriculture #Remote-Sensing Image Classification

paper · pdf · doi:10.48550/arxiv.2012.10393

openalex publication_date 2020/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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