Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion
Remote sensing super-resolution aims to enhance the spatial details of satellite images by introducing meaningful high-frequency features while avoiding hallucinations and spectral distortions. High-resolution imagery is usually not publicly available, whereas low-resolution imagery is freely availa...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10887321/ |
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| author | Simon Donike Cesar Aybar Luis Gomez-Chova Freddie Kalaitzis |
| author_facet | Simon Donike Cesar Aybar Luis Gomez-Chova Freddie Kalaitzis |
| author_sort | Simon Donike |
| collection | DOAJ |
| description | Remote sensing super-resolution aims to enhance the spatial details of satellite images by introducing meaningful high-frequency features while avoiding hallucinations and spectral distortions. High-resolution imagery is usually not publicly available, whereas low-resolution imagery is freely available with a much higher revisit rate, such as the Sentinel-2 multispectral imaging mission. Cross-sensor super-resolution has the potential to bridge this gap, providing high spatial and temporal resolution imagery which are otherwise unavailable for many remote sensing users and applications. With the recent advancements in diffusion models, many methodologies have emerged which take advantage of their generative power to perform super-resolution. We propose an adapted latent diffusion approach, since image diffusion is computationally prohibitive to be applied to large Earth observation datasets. Contrary to standard latent diffusion, we encode the low-resolution image to condition the diffusion process, forcing better spectral consistency with the input imagery. The model includes visible and near-infrared bands. To ensure trustworthy results, we utilize the probabilistic nature of diffusion models to generate pixel-level uncertainty maps. This confidence metric is crucial for real-world applications, such as environmental monitoring, land cover classification, and change detection, where accurate surface feature reconstruction and spectral consistency are essential. The uncertainty map allows users to evaluate the reliability of the product for these tasks. The proposed model super-resolves Sentinel-2 imagery at 10 to 2.5 m and is the first multispectral remote sensing (RS) super-resolution diffusion model efficient enough to process large-scale RS datasets, as well as the only model providing a pixelwise uncertainty metric. |
| format | Article |
| id | doaj-art-537dbbf0065c4db5a949e77280242360 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-537dbbf0065c4db5a949e772802423602025-08-20T03:01:27ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01186940695210.1109/JSTARS.2025.354222010887321Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent DiffusionSimon Donike0https://orcid.org/0000-0002-4440-3835Cesar Aybar1https://orcid.org/0000-0003-2745-9535Luis Gomez-Chova2https://orcid.org/0000-0003-3924-1269Freddie Kalaitzis3https://orcid.org/0000-0002-1471-646XImage Processing Laboratory (IPL), University of Valencia, Valencia, SpainImage Processing Laboratory (IPL), University of Valencia, Valencia, SpainImage Processing Laboratory (IPL), University of Valencia, Valencia, SpainUniversity of Oxford, Oxford, U.K.Remote sensing super-resolution aims to enhance the spatial details of satellite images by introducing meaningful high-frequency features while avoiding hallucinations and spectral distortions. High-resolution imagery is usually not publicly available, whereas low-resolution imagery is freely available with a much higher revisit rate, such as the Sentinel-2 multispectral imaging mission. Cross-sensor super-resolution has the potential to bridge this gap, providing high spatial and temporal resolution imagery which are otherwise unavailable for many remote sensing users and applications. With the recent advancements in diffusion models, many methodologies have emerged which take advantage of their generative power to perform super-resolution. We propose an adapted latent diffusion approach, since image diffusion is computationally prohibitive to be applied to large Earth observation datasets. Contrary to standard latent diffusion, we encode the low-resolution image to condition the diffusion process, forcing better spectral consistency with the input imagery. The model includes visible and near-infrared bands. To ensure trustworthy results, we utilize the probabilistic nature of diffusion models to generate pixel-level uncertainty maps. This confidence metric is crucial for real-world applications, such as environmental monitoring, land cover classification, and change detection, where accurate surface feature reconstruction and spectral consistency are essential. The uncertainty map allows users to evaluate the reliability of the product for these tasks. The proposed model super-resolves Sentinel-2 imagery at 10 to 2.5 m and is the first multispectral remote sensing (RS) super-resolution diffusion model efficient enough to process large-scale RS datasets, as well as the only model providing a pixelwise uncertainty metric.https://ieeexplore.ieee.org/document/10887321/Deep learninglatent diffusionmodel uncertaintyremote sensing (RS)Sentinel-2super-resolution (SR) |
| spellingShingle | Simon Donike Cesar Aybar Luis Gomez-Chova Freddie Kalaitzis Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning latent diffusion model uncertainty remote sensing (RS) Sentinel-2 super-resolution (SR) |
| title | Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion |
| title_full | Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion |
| title_fullStr | Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion |
| title_full_unstemmed | Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion |
| title_short | Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion |
| title_sort | trustworthy super resolution of multispectral sentinel 2 imagery with latent diffusion |
| topic | Deep learning latent diffusion model uncertainty remote sensing (RS) Sentinel-2 super-resolution (SR) |
| url | https://ieeexplore.ieee.org/document/10887321/ |
| work_keys_str_mv | AT simondonike trustworthysuperresolutionofmultispectralsentinel2imagerywithlatentdiffusion AT cesaraybar trustworthysuperresolutionofmultispectralsentinel2imagerywithlatentdiffusion AT luisgomezchova trustworthysuperresolutionofmultispectralsentinel2imagerywithlatentdiffusion AT freddiekalaitzis trustworthysuperresolutionofmultispectralsentinel2imagerywithlatentdiffusion |