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SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution.

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We introduce SEN2NAIP, a new dataset for training super-resolution (SR) models. This resource aids in enhancing the spatial resolution of Sentinel-2 (S2) satellite imagery for remote sensing applications.

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Area of Science:

  • Remote Sensing
  • Computer Vision

Background:

  • High spatial resolution is crucial for remote sensing.
  • Super-resolution (SR) algorithms are needed to upscale low-resolution (LR) images to high-resolution (HR).

Purpose of the Study:

  • To introduce SEN2NAIP, a novel and extensive dataset for training SR models.
  • To provide a valuable resource for enhancing Sentinel-2 (S2) imagery spatial resolution.

Main Methods:

  • Developed a dataset of 2,851 LR-HR image pairs from Sentinel-2 (S2) and National Agriculture Imagery Program (NAIP).
  • Created a degradation model to convert NAIP images to S2 characteristics.
  • Generated a second subset of 35,314 NAIP images and corresponding S2-like counterparts.

Main Results:

  • The SEN2NAIP dataset consists of two subsets, totaling 38,165 image pairs.
  • A cross-sensor degradation model was successfully developed and applied.
  • The dataset facilitates SR model training for Sentinel-2 imagery.

Conclusions:

  • SEN2NAIP is a comprehensive resource for advancing SR techniques in remote sensing.
  • The dataset supports research in improving the spatial resolution of Sentinel-2 data.