| Domain adaptation for the classification of remote sensing data: An overview of recent advances D Tuia, C Persello, L Bruzzone IEEE geoscience and remote sensing magazine 4 (2), 41-57, 2016 | 378 | 2016 |
| Batch-mode active-learning methods for the interactive classification of remote sensing images B Demir, C Persello, L Bruzzone IEEE Transactions on Geoscience and Remote Sensing 49 (3), 1014-1031, 2010 | 336 | 2010 |
| Active and Semisupervised Learning for the Classification of Remote Sensing Images C Persello, L Bruzzone IEEE Transactions on Geoscience and Remote Sensing 52 (11), 6937 - 6956, 2014 | 159 | 2014 |
| Deep fully convolutional networks for the detection of informal settlements in VHR images C Persello, A Stein IEEE geoscience and remote sensing letters 14 (12), 2325-2329, 2017 | 157 | 2017 |
| A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability L Bruzzone, C Persello IEEE transactions on geoscience and remote sensing 47 (9), 3180-3191, 2009 | 157 | 2009 |
| A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples L Bruzzone, C Persello IEEE Transactions on Geoscience and Remote Sensing 47 (7), 2142-2154, 2009 | 154 | 2009 |
| Active learning for domain adaptation in the supervised classification of remote sensing images C Persello, L Bruzzone IEEE Transactions on Geoscience and Remote Sensing 50 (11), 4468-4483, 2012 | 138 | 2012 |
| Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning C Persello, L Bruzzone IEEE transactions on geoscience and remote sensing 54 (5), 2615-2626, 2015 | 117 | 2015 |
| Detection of informal settlements from VHR images using convolutional neural networks N Mboga, C Persello, JR Bergado, A Stein Remote sensing 9 (11), 1106, 2017 | 115 | 2017 |
| A novel protocol for accuracy assessment in classification of very high resolution images C Persello, L Bruzzone IEEE transactions on geoscience and remote sensing 48 (3), 1232-1244, 2009 | 107 | 2009 |
| Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping C Persello, VA Tolpekin, JR Bergado, RA De By Remote sensing of environment 231, 111253, 2019 | 103 | 2019 |
| Informal settlement classification using point-cloud and image-based features from UAV data CM Gevaert, C Persello, R Sliuzas, G Vosselman ISPRS journal of photogrammetry and remote sensing 125, 225-236, 2017 | 88 | 2017 |
| The scope of earth-observation to improve the consistency of the SDG slum indicator M Kuffer, J Wang, M Nagenborg, K Pfeffer, D Kohli, R Sliuzas, C Persello ISPRS international journal of geo-information 7 (11), 428, 2018 | 76 | 2018 |
| Recurrent multiresolution convolutional networks for VHR image classification JR Bergado, C Persello, A Stein IEEE transactions on geoscience and remote sensing 56 (11), 6361-6374, 2018 | 68 | 2018 |
| Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework W Zhao, C Persello, A Stein ISPRS journal of photogrammetry and remote sensing 175, 119-131, 2021 | 67 | 2021 |
| Interactive Domain Adaptation for the Classification of Remote Sensing Images Using Active Learning C Persello IEEE Geoscience and Remote Sensing Letters, 1-5, 2013 | 64 | 2013 |
| Machine learning-based slum mapping in support of slum upgrading programs: The case of Bandung City, Indonesia G Leonita, M Kuffer, R Sliuzas, C Persello Remote sensing 10 (10), 1522, 2018 | 63 | 2018 |
| The temporal dynamics of slums employing a CNN-based change detection approach R Liu, M Kuffer, C Persello Remote sensing 11 (23), 2844, 2019 | 61 | 2019 |
| UAV in the advent of the twenties: Where we stand and what is next F Nex, C Armenakis, M Cramer, DA Cucci, M Gerke, E Honkavaara, ... ISPRS journal of photogrammetry and remote sensing 184, 215-242, 2022 | 60 | 2022 |
| Delineation of agricultural field boundaries from Sentinel-2 images using a novel super-resolution contour detector based on fully convolutional networks KM Masoud, C Persello, VA Tolpekin Remote sensing 12 (1), 59, 2019 | 59 | 2019 |
Lorenzo BruzzoneProfessor of Telecommunications, University of TrentoVerified email at ing.unitn.it
