Super-resolving multiresolution images with band-independant geometry of multispectral pixels
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Abstract (arXiv)
A new resolution enhancement method is presented for multispectral and multi-resolution images, such as these provided by the Sentinel-2 satellites. Starting from the highest resolution bands, band-dependent information (reflectance) is separated from information that is common to all bands (geometry of scene elements). This model is then applied to unmix low-resolution bands, preserving their reflectance, while propagating band-independent information to preserve the sub-pixel details. A reference implementation is provided, with an application example for super-resolving Sentinel-2 data.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2016.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.
4 papers.
- 2023Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 imagescited by 9
- 2022Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolutioncited by 6
- 2023OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing imagescited by 3
- 2025Land Surface Temperature Super-Resolution with a Scale-Invariance-Free Neural Approach: Application to MODIScited by 0
- 2025Land Surface Temperature Super-Resolution with a Scale-Invariance-Free Neural Approach: Application to MODIS3 Feb 2025
- 2023OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images14 Oct 2023
- 2023Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images26 Jan 2023
- 2022Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution22 Feb 2022