SUPER-RESOLUTION FOR SENTINEL-2 IMAGES

11 citations as recorded by crossref.

1. Deep learning-based super-resolution for harmful algal bloom monitoring of inland water D. Kwon et al. 10.1080/15481603.2023.2249753
2. How Does Super-Resolution for Satellite Imagery Affect Different Types of Land Cover? Sentinel-2 Case A. Malczewska & M. Wielgosz 10.1109/JSTARS.2023.3328997
3. Revisiting Remote Sensing Cross-Sensor Single Image Super-Resolution: The Overlooked Impact of Geometric and Radiometric Distortion J. Michel et al. 10.1109/TGRS.2025.3572548
4. SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms J. Michel et al. 10.3390/data7070096
5. High resolution olivine abundance mapping in the Copernicus crater combining spectral data of Chandrayaan-1 M3 and Kaguya MI Y. Surkov et al. 10.1016/j.pss.2025.106039
6. X-SVM: An Extension of C-SVM Algorithm for Classification of High-Resolution Satellite Imagery G. Lantzanakis et al. 10.1109/TGRS.2020.3017937
7. Remote sensing applications for reservoir water level monitoring, sustainable water surface management, and environmental risks in Quang Nam province, Vietnam D. Quang et al. 10.2166/wcc.2021.347
8. Automatic Generation of Aerial Orthoimages Using Sentinel-2 Satellite Imagery with a Context-Based Deep Learning Approach S. Yoo et al. 10.3390/app11031089
9. A Comprehensive Survey of Super-Resolution Remote Sensing Image Datasets: Evolution, Challenges, and Future Directions A. Vu-Duc et al. 10.1109/ACCESS.2025.3599535
10. Deep learning-based harmonization and super-resolution of Landsat-8 and Sentinel-2 images V. Sambandham et al. 10.1016/j.isprsjprs.2024.04.026
11. Enhancing Historical Aerial Photographs: A New Approach Based on Non-Reference Metric and Photo Interpretation Elements A. Incekara & D. Seker 10.3390/s25072126