SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS

79 citations as recorded by crossref.

1. Automatic Generation of Aerial Orthoimages Using Sentinel-2 Satellite Imagery with a Context-Based Deep Learning Approach S. Yoo et al. https://doi.org/10.3390/app11031089
2. A novel generative adversarial network framework for super-resolution reconstruction of remote sensing R. Li et al. https://doi.org/10.3389/feart.2025.1578321
3. A Prediction-to-Prediction Remote Sensing Image Super-Resolution Network under a Multi-Level Supervision Paradigm J. Guo et al. https://doi.org/10.3390/app132111827
4. Landsat-8 to Sentinel-2 Satellite Imagery Super-Resolution-Based Multiscale Dilated Transformer Generative Adversarial Networks C. Wang et al. https://doi.org/10.3390/rs15225272
5. Optically Enhanced Super-Resolution of Sea Surface Temperature Using Deep Learning D. Lloyd et al. https://doi.org/10.1109/TGRS.2021.3094117
6. A comparative study of deep learning methods for super-resolution of NPP-VIIRS nighttime light images C. Zhang et al. https://doi.org/10.1016/j.jag.2025.104995
7. Lightweight Edge-Guided Super-Resolution Network for Remote Sensing Images Y. Wang et al. https://doi.org/10.1109/TGRS.2025.3571224
8. MSWAGAN: Multispectral Remote Sensing Image Super-Resolution Based on Multiscale Window Attention Transformer C. Wang et al. https://doi.org/10.1109/TGRS.2024.3385752
9. Joint Sparse Representation-based Single Image Super-Resolution for Remote Sensing Applications B. Deka et al. https://doi.org/10.1109/JSTARS.2023.3244069
10. Digital Surface Model and Fractal-Guided Multi-Directional Network for Remote Sensing Image Super-Resolution S. Li et al. https://doi.org/10.3390/info16121020
11. 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
12. Semantic guided large scale factor remote sensing image super-resolution with generative diffusion prior C. Wang & W. Sun https://doi.org/10.1016/j.isprsjprs.2024.12.001
13. Advancing image super-resolution techniques in remote sensing: A comprehensive survey Y. Qi et al. https://doi.org/10.1016/j.isprsjprs.2025.10.024
14. Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review K. Karwowska & D. Wierzbicki https://doi.org/10.1109/JSTARS.2022.3167646
15. Single-Image Superresolution for RGB Remote Sensing Imagery via Multiscale CNN-Transformer Feature Fusion X. Yao et al. https://doi.org/10.1109/JSTARS.2024.3504865
16. Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network S. Mei et al. https://doi.org/10.3390/rs9111139
17. Towards the next generation of Geospatial Artificial Intelligence G. Mai et al. https://doi.org/10.1016/j.jag.2025.104368
18. Remote sensing image super-resolution using multi-scale convolutional sparse coding network R. Cheng et al. https://doi.org/10.1371/journal.pone.0276648
19. Super Resolution by Deep Learning Improves Boulder Detection in Side Scan Sonar Backscatter Mosaics P. Feldens https://doi.org/10.3390/rs12142284
20. ACT-SR: Aggregation Connection Transformer for Remote Sensing Image Super-Resolution Y. Kang et al. https://doi.org/10.1109/JSTARS.2024.3506717
21. Unsupervised Deep Image Prior‐Based Neural Networks for Single Image Super‐Resolution: Comparative Analysis and Modelling Guidelines A. Abalo‐García et al. https://doi.org/10.1111/exsy.70142
22. HybridATNet: Multi-Scale Attention and Hybrid Feature Refinement Network for Remote Sensing Image Super-Resolution N. Sultan et al. https://doi.org/10.1109/ACCESS.2025.3608009
23. TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers T. An et al. https://doi.org/10.1109/JSTARS.2022.3143532
24. A plexus‐convolutional neural network framework for fast remote sensing image super‐resolution in wavelet domain F. Deeba et al. https://doi.org/10.1049/ipr2.12136
25. Learning Deep Resonant Prior for Hyperspectral Image Super-Resolution Z. Gong et al. https://doi.org/10.1109/TGRS.2022.3185647
26. Detail-Optimized Super-Resolution Reconstruction-Based Multistage Training Strategy for Remote Sensing Semantic Segmentation B. Sui et al. https://doi.org/10.1109/TGRS.2023.3339842
27. RAN: Region-Aware Network for Remote Sensing Image Super-Resolution B. Liu et al. https://doi.org/10.1109/TGRS.2023.3330876
28. A Remote Sensing Image Super-Resolution Reconstruction Model Combining Multiple Attention Mechanisms Y. Xu et al. https://doi.org/10.3390/s24144492
29. Single-Image Super-Resolution for Remote Sensing Images Using a Deep Generative Adversarial Network With Local and Global Attention Mechanisms Y. Li et al. https://doi.org/10.1109/TGRS.2021.3093043
30. Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images Y. Chang et al. https://doi.org/10.3390/rs15123139
31. Super-Resolution GAN and Global Aware Object Detection System for Vehicle Detection in Complex Traffic Environments H. Wang et al. https://doi.org/10.1109/ACCESS.2024.3442484
32. Dual-domain prior unfolding network for remote sensing image super-resolution J. Dong et al. https://doi.org/10.1007/s12145-024-01500-6
33. S2Transformer: Exploring Sparsity in Remote Sensing Images for Efficient Super-Resolution Z. Zhang et al. https://doi.org/10.3390/s25185643
34. A benchmark dataset for Landsat-to-Sentinel image generation using deep learning-driven super-resolution techniques P. Wang et al. https://doi.org/10.1016/j.asr.2026.01.049
35. Remote Sensing Image Super-Resolution With Residual Split Attention Mechanism X. Chen et al. https://doi.org/10.1109/JSTARS.2023.3287894
36. Deep learning for single-image super-resolution in remote sensing: a review S. Yang et al. https://doi.org/10.1080/01431161.2025.2600100
37. CDEHAT: Conditional Diffusion-Assisted Enhanced Hybrid Attention Transformer for remote sensing imagery super-resolution X. Wu et al. https://doi.org/10.1016/j.isprsjprs.2026.03.002
38. Effectiveness of Unmanned Aerial Vehicle-Based LiDAR for Assessing the Impact of Catastrophic Windstorm Events on Timberland D. Badal et al. https://doi.org/10.3390/drones9110756
39. A CNN-Transformer Embedded Unfolding Network for Hyperspectral Image Super-Resolution Y. Tang et al. https://doi.org/10.1109/TGRS.2024.3431924
40. A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images X. Wang et al. https://doi.org/10.3390/rs15205062
41. From Artifact Removal to Super-Resolution J. Wang et al. https://doi.org/10.1109/TGRS.2022.3196709
42. Dual Self-Attention Swin Transformer for Hyperspectral Image Super-Resolution Y. Long et al. https://doi.org/10.1109/TGRS.2023.3275146
43. Deep learning-based remote sensing image super-resolution: Recent advances and challenges J. Yang et al. https://doi.org/10.1016/j.neucom.2026.132939
44. Spectral-Spatial MLP Network for Hyperspectral Image Super-Resolution Y. Yao et al. https://doi.org/10.3390/rs15123066
45. SBHSR: single-band super-resolution method for hyperspectral images based on blind degradation and fusion of auxiliary band L. Bu et al. https://doi.org/10.1080/10106049.2023.2294901
46. The Spatiotemporal Fusion for Nighttime Light Based on SDGSAT-1 and VIIRS Data S. Zhang et al. https://doi.org/10.1109/TGRS.2025.3560092
47. Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement G. Tsagkatakis et al. https://doi.org/10.3390/s19183929
48. Double Prior Network for Multidegradation Remote Sensing Image Super-Resolution M. Shi et al. https://doi.org/10.1109/JSTARS.2023.3242053
49. ConvFormerSR: Fusing Transformers and Convolutional Neural Networks for Cross-Sensor Remote Sensing Imagery Super-Resolution J. Li et al. https://doi.org/10.1109/TGRS.2023.3340043
50. Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution X. Dong et al. https://doi.org/10.3390/rs11232857
51. SFSIN: A Lightweight Model for Remote Sensing Image Super-Resolution with Strip-like Feature Superpixel Interaction Network Y. Lyu et al. https://doi.org/10.3390/math13111720
52. SFEFeNet: A Structure-Frequency Mutual-Guided Lightweight Network for Remote Sensing Image Super-Resolution R. Liu et al. https://doi.org/10.3390/rs18132102
53. Deep Learning-Based Single Image Super-Resolution: An Investigation for Dense Scene Reconstruction with UAS Photogrammetry M. Pashaei et al. https://doi.org/10.3390/rs12111757
54. A Hybrid Spectral Attention-Enabled Multiscale Spatial–Spectral Learning Network for Hyperspectral Image Superresolution W. Wang et al. https://doi.org/10.1109/JSTARS.2024.3407953
55. CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution W. Liang & Y. Liu https://doi.org/10.3390/rs17142513
56. A Spectral Grouping and Attention-Driven Residual Dense Network for Hyperspectral Image Super-Resolution D. Liu et al. https://doi.org/10.1109/TGRS.2021.3049875
57. Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms P. Burdziakowski https://doi.org/10.3390/rs12050810
58. Single hyperspectral image super-resolution using a progressive upsampling deep prior network H. Wang et al. https://doi.org/10.3934/era.2024205
59. Paradigm shifts in super-resolution techniques for remote sensing applications G. Rohith & L. Kumar https://doi.org/10.1007/s00371-020-01957-8
60. Intensifying the spatial resolution of 3D thermal models from aerial imagery using deep learning-based image super-resolution A. Fallah et al. https://doi.org/10.1080/10106049.2022.2082544
61. Super-Resolution of Sentinel-2 Images Using a Spectral Attention Mechanism M. Zabalza & A. Bernardini https://doi.org/10.3390/rs14122890
62. Degradation learning for unsupervised hyperspectral image super-resolution based on generative adversarial network S. Zhang et al. https://doi.org/10.1007/s11760-021-01902-9
63. RGTGAN: Reference-Based Gradient-Assisted Texture-Enhancement GAN for Remote Sensing Super-Resolution Z. Tu et al. https://doi.org/10.1109/TGRS.2024.3359095
64. A Review of Image Super-Resolution Approaches Based on Deep Learning and Applications in Remote Sensing X. Wang et al. https://doi.org/10.3390/rs14215423
65. Efficient Single-image Super-resolution Using Dual path Connections with Multiple scale Learning B. Yang & G. Wu https://doi.org/10.1145/3570164
66. Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks X. Wang et al. https://doi.org/10.3390/rs16224252
67. Multimedia super-resolution via deep learning: A survey K. Hayat https://doi.org/10.1016/j.dsp.2018.07.005
68. Learning hyperspectral images from RGB images via a coarse-to-fine CNN S. Mei et al. https://doi.org/10.1007/s11432-020-3102-9
69. DESR: Super-Resolution Reconstruction of FengYun-4 Multispectral Images X. Kan et al. https://doi.org/10.1109/JSTARS.2025.3645116
70. Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique A. Incekara et al. https://doi.org/10.3390/app14041495
71. Dual-Domain Optimization Model Based on Discrete Fourier Transform and Frequency-Domain Fusion for Remote Sensing Single-Image Super-Resolution L. Shi et al. https://doi.org/10.1109/TGRS.2025.3540504
72. 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
73. 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
74. Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review K. Hasan et al. https://doi.org/10.1109/JSTARS.2023.3328315
75. Remote Sensing Image Pansharpening Using Deep Internal Learning With Residual Double-Attention Network R. Sustika et al. https://doi.org/10.1109/ACCESS.2024.3481466
76. Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks F. Salvetti et al. https://doi.org/10.3390/rs12142207
77. InstanceSR: Efficient Reconstructing Small Object With Differential Instance-Level Super-Resolution Y. Fan et al. https://doi.org/10.1109/TCSVT.2024.3496664
78. FATSNet: transformer-based skip network with frequency attention for remote sensing image super-resolution Y. Huo et al. https://doi.org/10.1007/s40747-026-02245-z
79. Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution I. Ismail et al. https://doi.org/10.32604/cmc.2023.043873