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- W2991962514 abstract "Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing tasks. However, deep learning for multi-image superresolution from multitemporal imagery has received little attention so far. We propose a residual CNN that exploits both spatial and temporal correlations in the low-resolution image set by using 3D convolutional layers to combine multiple images from the same scene. The experiments have been carried out using a dataset of PROBA-V satellite ground images, composed of several low-resolution and high-resolution images taken at different times from instruments on the same platform, in the context of a challenge issued by the European Space Agency." @default.
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- W2991962514 date "2019-09-01" @default.
- W2991962514 modified "2023-10-16" @default.
- W2991962514 title "Deep Learning For Super-Resolution Of Unregistered Multi-Temporal Satellite Images" @default.
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- W2991962514 doi "https://doi.org/10.1109/whispers.2019.8920910" @default.
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