| Pansharpening by convolutional neural networks G Masi, D Cozzolino, L Verdoliva, G Scarpa Remote Sensing 8 (7), 594, 2016 | 878 | 2016 |
| Target-adaptive CNN-based pansharpening G Scarpa, S Vitale, D Cozzolino IEEE Transactions on Geoscience and Remote Sensing 56 (9), 5443-5457, 2018 | 341 | 2018 |
| A new benchmark based on recent advances in multispectral pansharpening: Revisiting pansharpening with classical and emerging pansharpening methods G Vivone, M Dalla Mura, A Garzelli, R Restaino, G Scarpa, MO Ulfarsson, ... IEEE Geoscience and Remote Sensing Magazine 9 (1), 53-81, 2020 | 211 | 2020 |
| A tree-structured Markov random field model for Bayesian image segmentation C D'Elia, G Poggi, G Scarpa IEEE Transactions on Image processing 12 (10), 1259-1273, 2003 | 191 | 2003 |
| Fast adaptive nonlocal SAR despeckling D Cozzolino, S Parrilli, G Scarpa, G Poggi, L Verdoliva IEEE Geoscience and Remote Sensing Letters 11 (2), 524-528, 2013 | 179 | 2013 |
| A CNN-based fusion method for feature extraction from sentinel data G Scarpa, M Gargiulo, A Mazza, R Gaetano Remote Sensing 10 (2), 236, 2018 | 152 | 2018 |
| Marker-controlled watershed-based segmentation of multiresolution remote sensing images R Gaetano, G Masi, G Poggi, L Verdoliva, G Scarpa IEEE Transactions on Geoscience and Remote Sensing 53 (6), 2987-3004, 2014 | 127 | 2014 |
| Supervised segmentation of remote sensing images based on a tree-structured MRF model G Poggi, G Scarpa, JB Zerubia IEEE Transactions on geoscience and remote sensing 43 (8), 1901-1911, 2005 | 124 | 2005 |
| Hierarchical texture-based segmentation of multiresolution remote-sensing images R Gaetano, G Scarpa, G Poggi IEEE Transactions on geoscience and remote sensing 47 (7), 2129-2141, 2009 | 110 | 2009 |
| Hierarchical multiple Markov chain model for unsupervised texture segmentation G Scarpa, R Gaetano, M Haindl, J Zerubia IEEE Transactions on Image Processing 18 (8), 1830-1843, 2009 | 80 | 2009 |
| Machine learning in pansharpening: A benchmark, from shallow to deep networks LJ Deng, G Vivone, ME Paoletti, G Scarpa, J He, Y Zhang, J Chanussot, ... IEEE Geoscience and Remote Sensing Magazine 10 (3), 279-315, 2022 | 78 | 2022 |
| Multitemporal SAR image despeckling based on block-matching and collaborative filtering G Chierchia, M El Gheche, G Scarpa, L Verdoliva IEEE Transactions on Geoscience and Remote Sensing 55 (10), 5467-5480, 2017 | 71 | 2017 |
| Detection of environmental hazards through the feature-based fusion of optical and SAR data: A case study in southern Italy A Errico, CV Angelino, L Cicala, G Persechino, C Ferrara, M Lega, ... International Journal of Remote Sensing 36 (13), 3345-3367, 2015 | 64 | 2015 |
| Nonlocal CNN SAR image despeckling D Cozzolino, L Verdoliva, G Scarpa, G Poggi Remote Sensing 12 (6), 1006, 2020 | 63 | 2020 |
| Deep learning methods for synthetic aperture radar image despeckling: An overview of trends and perspectives G Fracastoro, E Magli, G Poggi, G Scarpa, D Valsesia, L Verdoliva IEEE Geoscience and Remote Sensing Magazine 9 (2), 29-51, 2021 | 53 | 2021 |
| A CNN-based coherence-driven approach for InSAR phase unwrapping F Sica, F Calvanese, G Scarpa, P Rizzoli IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2020 | 52 | 2020 |
| A nonlocal approach for SAR image denoising S Parrilli, M Poderico, CV Angelino, G Scarpa, L Verdoliva 2010 IEEE International Geoscience and Remote Sensing Symposium, 726-729, 2010 | 51 | 2010 |
| A detail-preserving cross-scale learning strategy for CNN-based pansharpening S Vitale, G Scarpa Remote Sensing 12 (3), 348, 2020 | 50 | 2020 |
| Fast super-resolution of 20 m Sentinel-2 bands using convolutional neural networks M Gargiulo, A Mazza, R Gaetano, G Ruello, G Scarpa Remote Sensing 11 (22), 2635, 2019 | 50 | 2019 |
| Pansharpening by convolutional neural networks in the full resolution framework M Ciotola, S Vitale, A Mazza, G Poggi, G Scarpa IEEE Transactions on Geoscience and Remote Sensing 60, 1-17, 2022 | 44 | 2022 |
Giovanni PoggiUniversity Federico II of Naples, ITALYDirección de correo verificada de unina.it
