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Article Dans Une Revue IEEE Geoscience and Remote Sensing Letters Année : 2023

Super-resolution by fusing multi-spectral and terrain models: application to water level mapping

Résumé

Recently, deep convolutional networks have made great progress on the task of super resolution, i.e. reconstructing images with finer spatial resolution. However, although the reconstructions are visually impressive, they may lack physical consistency. This aspect is sought in remote sensing, where the resolution of satellite imagery (e.g. Sentinel-2) may be too coarse to characterize the physical structure and dynamics of certain landscapes. Through the study of flooding dynamics in wet grasslands, we propose a super resolution approach that allows deriving fine resolution patterns that are visually realistic and physically exploitable. This approach is based on an architecture, Fusion-UNet, allowing the fusion of multispectral data with a digital terrain model (DTM) associated with a loss function combining content, structure and segmentation losses. Our results show that this model can precisely predict water levels while restituting the fine structure of the landscape. This approach allows to refine the production of hydrological and ecological indicators to define the state of the ecosystem.
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Dates et versions

hal-04310773 , version 1 (27-11-2023)

Identifiants

Citer

Emilien Alvarez-Vanhard, Fernandez-Garcia Guglielmo, Thomas Corpetti. Super-resolution by fusing multi-spectral and terrain models: application to water level mapping. IEEE Geoscience and Remote Sensing Letters, 2023, 20, pp.1-5. ⟨10.1109/LGRS.2023.3319548⟩. ⟨hal-04310773⟩
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