# Super-resolving multiresolution images with band-independant geometry of multispectral pixels

arXiv: https://arxiv.org/abs/1609.07986 · HTML: https://mlsanity.com/paper/1609.07986/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images](https://mlsanity.com/paper/2301.11154/) (2023)
- [Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution](https://mlsanity.com/paper/2202.10753/) (2022)
- [OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images](https://mlsanity.com/paper/2310.09517/) (2023)
- [Land Surface Temperature Super-Resolution with a Scale-Invariance-Free Neural Approach: Application to MODIS](https://mlsanity.com/paper/2502.01204/) (2025)

## 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.