DeepSen3: Deep multi-scale learning model for spatial-spectral fusion of Sentinel-2 and Sentinel-3 remote sensing images - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

DeepSen3: Deep multi-scale learning model for spatial-spectral fusion of Sentinel-2 and Sentinel-3 remote sensing images

Résumé

Recently, deep learning methods that integrate image features gradually became a hot development trend in fusion of multispectral and hyperspectral remote sensing images, aka multi-sharpening. Fusion of a low spatial resolution hyperspectral image (LR-HSI datacube) with its corresponding high spatial resolution multispectral image (HR-MSI datacube) to reconstruct a high spatial resolution hyperspectral image (HR-HSI) has been a significant subject in recent years. Nevertheless, it is still difficult to achieve a high quality of spatial and spectral information fusion. In this paper, we propose a Deep Multi-Scale Learning Model (called DeepSen3) of spatial-spectral information fusion based on multi-scale inception residual convolutional neural network (CNN) for more efficient hyperspectral and multispectral image fusion from ESA remote sensing satellite missions (Sentinel-2 and Sentinel-3 images). The proposed DeepSen3 fusion network was applied to Sentinel-2 MSI (13 spectral bands with a spatial resolution ranging from 10, 20 to 60 m) and Sentinel-3 OLCI (21 spectral bands with a spatial resolution of 300 m) images. Extensive experiments demonstrate that the proposed DeepSen3 network achieves the best performance (both qualitatively and quantitatively) compared with recent state-of-the-art deep learning approaches.
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Dates et versions

hal-03714846 , version 1 (13-06-2023)

Identifiants

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Ahed Alboody, Matthieu Puigt, Gilles Roussel, Vincent Vantrepotte, Cédric Jamet, et al.. DeepSen3: Deep multi-scale learning model for spatial-spectral fusion of Sentinel-2 and Sentinel-3 remote sensing images. 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2022, Sep 2022, Rome, Italy. ⟨10.1109/WHISPERS56178.2022.9955139⟩. ⟨hal-03714846⟩
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