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
2. Advancing image super-resolution techniques in remote sensing: A comprehensive survey Y. Qi et al.
3. A comprehensive review on deep learning based remote sensing image super-resolution methods P. Wang et al.
4. Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks L. Salgueiro et al.
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.
6. The Effect of Super Resolution Method on Classification Performance of Satellite Images A. CENGİZ & D. AVCI
7. How Does Super-Resolution for Satellite Imagery Affect Different Types of Land Cover? Sentinel-2 Case A. Malczewska & M. Wielgosz
8. Super-Resolution of Sentinel-2 Images Using a Spectral Attention Mechanism M. Zabalza & A. Bernardini
9. VegGAN: A Generative Adversarial Network for Downscaling JPSS/VIIRS Vegetation Indices Y. Yang et al.
10. Super-Resolution of Sentinel-2 Images Using Convolutional Neural Networks and Real Ground Truth Data M. Galar et al.
11. Enhanced satellite image resolution with a residual network and correlation filter A. Sharma et al.
12. Deep learning-based Sentinel-2 super-resolution via channel attention and high-frequency feature enhancement K. Nguyen-Vi et al.