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Upsampling (Sentinel-2)
Description
An algorithm that improves the spatial resolution of RGB bands in Sentinel-2 imagery. Upsampling can be used as a pre-processing step before change detection, vegetation detection, infrastructure detection, fire risk estimation, and land use management.
Technical information
| Source | Sentinel-2 catalog collection |
| Compatibility | - The STAC item should be a Sentinel-2 image - The STAC item should have been added to storage in 2023 or later - The STAC item must be a supported data product |
| Required parameters | - Console: If your chosen input data meets the above specifications you will be able to run your job - API: You will need to input an Output title and the Input data |
| Output data format | GeoTIFF file with an upsampled spatial resolution of 3.20 m |
| Output | The result will be added to your account as a STAC item in a new STAC collection. You can retrieve the resulting data in one of the following ways: - Open the console, go to Data management → Jobs - Retrieve the results via the API |
Algorithm performance and training data
Tensorflow is used to build a convolutional neural network (CNN) for super-resolution. Unlike other conventional super-resolution networks, high-resolution and low-resolution images are from different satellites.
It helps users to have a clearer view of their areas of interest at a much lower cost compared to commercial imagery.
Limitation: Super-resolution cannot express features that are not shown in the original image.
Further information
For more information, please visit the technical documentation.