hvn2 / S2_SSC_CNN

Codes for the paper "Zero-shot Sentinel 2 sharpening using a symmetric skipped connection convolutional neural network" in Proceeding of IGRASS 2020.

S2_SSS_CNN

Official Python codes for the paper "Zero-shot {Sentinel-2} Sharpening Using A Symmetric Skipped Connection Convolutional Neural Network" in Proceeding of IGARSS 2020 (Virtual Symposion), September 2020.

Authors: Han V. Nguyen $^\ast \dagger$, Magnus O. Ulfarsson $^\ast$, Johannes R. Sveinsson $^\ast$, and Jakob Sigurdsson $^\ast$
$^\ast$ Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
$^\dagger$ Department of Electrical and Electronic Engineering, Nha Trang University, Khanh Hoa, Vietnam
Email: [e-mail]

Abstract

Sentinel-2 (S2) satellite constellations can provide multispectral images of 10 m, 20 m, and 60 m resolution for visible, near-infrared (NIR) and short-wave infrared (SWIR) in the electromagnetic spectrum. In this paper, we present a sharpening method based on a symmetric skipped connection convolutional neural network, called SSC-CNN, to sharpen 20 m bands using 10 m bands. The main advantage of SSC-CNN architecture is that it brings the features of the input branch to the output, thus improving convergence without using too many deep layers. The proposed method uses the reduced-scale combination of 10 m bands and 20 m bands, and the observed 20 m bands as the training pairs. The experimental results using two Sentinel-2 datasets show that our method outperforms competitive methods in quantitative metrics and visualization.

Please cite our work if you are interested

@inproceedings{nguyen2020zero, title={Zero-shot sentinel-2 sharpening using a symmetric skipped connection convolutional neural network}, author={Nguyen, Han V and Ulfarsson, Magnus O and Sveinsson, Johannes R and Sigurdsson, Jakob}, booktitle={IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium}, pages={613--616}, year={2020}, organization={IEEE} }

Usage:

Run the jupyter notebook file and see the results.

• Data (preprocessing in Matlab) are in folder data
  • Two real datasets are: coastal in coastalA_cell_RR.mat, reykjavik in reykjavik_cell_RR.mat
  • The data are in reduced scale
• CNN models are in folder models
• Results prenstented in the paper are in folder results

Enviroment:

• Tensorflow 2.0
• Numpy
• Scipy, Skimage