| A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain F Bovolo, L Bruzzone IEEE Transactions on Geoscience and Remote Sensing 45 (1), 218-236, 2006 | 664 | 2006 |
| A detail-preserving scale-driven approach to change detection in multitemporal SAR images F Bovolo, L Bruzzone IEEE transactions on geoscience and remote sensing 43 (12), 2963-2972, 2005 | 514 | 2005 |
| Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art P Ghamisi, B Rasti, N Yokoya, Q Wang, B Hofle, L Bruzzone, F Bovolo, ... IEEE Geoscience and Remote Sensing Magazine 7 (1), 6-39, 2019 | 489 | 2019 |
| Unsupervised deep change vector analysis for multiple-change detection in VHR images S Saha, F Bovolo, L Bruzzone IEEE transactions on geoscience and remote sensing 57 (6), 3677-3693, 2019 | 466 | 2019 |
| A novel framework for the design of change-detection systems for very-high-resolution remote sensing images L Bruzzone, F Bovolo Proceedings of the IEEE 101 (3), 609-630, 2012 | 358 | 2012 |
| Fast and robust matching for multimodal remote sensing image registration Y Ye, L Bruzzone, J Shan, F Bovolo, Q Zhu IEEE Transactions on Geoscience and Remote Sensing 57 (11), 9059-9070, 2019 | 348 | 2019 |
| A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images F Bovolo, S Marchesi, L Bruzzone Geoscience and Remote Sensing, IEEE Transactions on 50 (6), 2196 - 2212, 2012 | 348 | 2012 |
| A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure F Bovolo, L Bruzzone, M Marconcini IEEE transactions on geoscience and remote sensing 46 (7), 2070-2082, 2008 | 345 | 2008 |
| A split-based approach to unsupervised change detection in large-size multitemporal images: Application to tsunami-damage assessment F Bovolo, L Bruzzone IEEE Transactions on Geoscience and Remote Sensing 45 (6), 1658-1670, 2007 | 337 | 2007 |
| Supervised change detection in VHR images using contextual information and support vector machines M Volpi, D Tuia, F Bovolo, M Kanevski, L Bruzzone International Journal of Applied Earth Observation and Geoinformation 20, 77-85, 2013 | 333 | 2013 |
| A review of change detection in multitemporal hyperspectral images: Current techniques, applications, and challenges S Liu, D Marinelli, L Bruzzone, F Bovolo IEEE Geoscience and Remote Sensing Magazine 7 (2), 140-158, 2019 | 329 | 2019 |
| Semisupervised one-class support vector machines for classification of remote sensing data J Mũnoz-Marí, F Bovolo, L Gómez-Chova, L Bruzzone, G Camp-Valls IEEE transactions on geoscience and remote sensing 48 (8), 3188-3197, 2010 | 319 | 2010 |
| Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach B Demir, F Bovolo, L Bruzzone IEEE Transactions on Geoscience and Remote Sensing 51 (1), 300-312, 2012 | 224 | 2012 |
| A multilevel parcel-based approach to change detection in very high resolution multitemporal images F Bovolo IEEE Geoscience and Remote Sensing Letters 6 (1), 33-37, 2008 | 211 | 2008 |
| Building change detection in multitemporal very high resolution SAR images C Marin, F Bovolo, L Bruzzone IEEE transactions on geoscience and remote sensing 53 (5), 2664-2682, 2014 | 205 | 2014 |
| Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images S Liu, L Bruzzone, F Bovolo, M Zanetti, P Du IEEE transactions on geoscience and remote sensing 53 (8), 4363-4378, 2015 | 196 | 2015 |
| A context-sensitive technique for unsupervised change detection based on Hopfield-type neural networks S Ghosh, L Bruzzone, S Patra, F Bovolo, A Ghosh IEEE Transactions on Geoscience and Remote Sensing 45 (3), 778-789, 2007 | 186 | 2007 |
| The time variable in data fusion: A change detection perspective F Bovolo, L Bruzzone IEEE Geoscience and Remote Sensing Magazine 3 (3), 8-26, 2015 | 178 | 2015 |
| Change detection from very-high-spatial-resolution optical remote sensing images: Methods, applications, and future directions D Wen, X Huang, F Bovolo, J Li, X Ke, A Zhang, JA Benediktsson IEEE Geoscience and Remote Sensing Magazine 9 (4), 68-101, 2021 | 174 | 2021 |
| Building change detection in VHR SAR images via unsupervised deep transcoding S Saha, F Bovolo, L Bruzzone IEEE Transactions on Geoscience and Remote Sensing 59 (3), 1917-1929, 2020 | 174 | 2020 |
Lorenzo BruzzoneProfessor of Telecommunications, University of Trento在 ing.unitn.it 的電子郵件地址已通過驗證