DEEP RESIDUAL LEARNING FOR SINGLE-IMAGE SUPER-RESOLUTION OF MULTI-SPECTRAL SATELLITE IMAGERY

12 citations as recorded by crossref.

1. Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review K. Karwowska & D. Wierzbicki https://doi.org/10.1109/JSTARS.2022.3167646
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3. A comprehensive review on deep learning based remote sensing image super-resolution methods P. Wang et al. https://doi.org/10.1016/j.earscirev.2022.104110
4. Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks L. Salgueiro et al. https://doi.org/10.3390/rs13245007
5. S3-ESRGAN: Enhanced Super-Resolution Generative Adversarial Network for Remote Sensing Imagery Spatial Resolution Improvement—An Application Using Sentinel-2 and UAV Images A. Toosi et al. https://doi.org/10.1109/JSTARS.2025.3640940
6. The Effect of Super Resolution Method on Classification Performance of Satellite Images A. CENGİZ & D. AVCI https://doi.org/10.55525/tjst.1252420
7. How Does Super-Resolution for Satellite Imagery Affect Different Types of Land Cover? Sentinel-2 Case A. Malczewska & M. Wielgosz https://doi.org/10.1109/JSTARS.2023.3328997
8. Super-Resolution of Sentinel-2 Images Using a Spectral Attention Mechanism M. Zabalza & A. Bernardini https://doi.org/10.3390/rs14122890
9. VegGAN: A Generative Adversarial Network for Downscaling JPSS/VIIRS Vegetation Indices Y. Yang et al. https://doi.org/10.1109/JSTARS.2025.3588052
10. Super-Resolution of Sentinel-2 Images Using Convolutional Neural Networks and Real Ground Truth Data M. Galar et al. https://doi.org/10.3390/rs12182941
11. Enhanced satellite image resolution with a residual network and correlation filter A. Sharma et al. https://doi.org/10.1016/j.chemolab.2024.105277
12. Deep learning-based Sentinel-2 super-resolution via channel attention and high-frequency feature enhancement K. Nguyen-Vi et al. https://doi.org/10.3389/frsen.2025.1644460