Publications
International Journals
Wen Zhou, Claudio Persello, Mengmeng Li, Alfred Stein,
Building use and mixed-use classification with a transformer-based network fusing satellite images and geospatial textual information,
Remote Sensing of Environment, Vol 297, November 2023
https://www.sciencedirect.com/science/article/pii/S0034425723003188
C. Persello, J. Grift, X. Fan, C. Paris, R. Hänsch, M. Koeva, and A. Nelson,
AI4SmallFarms: A Data Set for Crop Field Delineation in Southeast Asian Smallholder Farms. IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023.
Xianwei Lv, Claudio Persello, Wufan Zhao, Xiao Huang, Zhongwen Hu, Dongping Ming, Alfred Stein,
Pruning for image segmentation: Improving computational efficiency for large-scale remote sensing applications,
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 202, 2023, Pages 13-29, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2023.05.024.
https://www.sciencedirect.com/science/article/pii/S0924271623001417
W. Zhao, C. Persello and A. Stein
Semantic-aware Unsupervised Domain Adaptation for Height Estimation from Single-view Aerial Images
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 196, February 2023, Pages 372-385
https://www.sciencedirect.com/science/article/pii/S0924271623000096
Farsad Layegh, N., Darvishzadeh, R., Skidmore, A. K., Persello, C. & Kruger, N.,
Integrating semi-supervised learning with an expert system for vegetation cover classification using Sentinel-2 and RapidEye data
Remote sensing. 14, 15, p. 1-18 18 p., 3605.
https://www.mdpi.com/2072-4292/14/15/3605
W. Zhao, C. Persello and A. Stein
Extracting Planar Roof Structures from Very High Resolution Images using Graph Neural Networks
ISPRS Journal of Photogrammetry and Remote Sensing, 187 (May 2022), 215–242
https://www.sciencedirect.com/science/article/pii/S092427162200065X
C. Persello, J.D. Wegner, R. Hänsch, D. Tuia, P. Ghamisi, M. Koeva, and G. Camps-Valls
Deep Learning and Earth Observation to Support the Sustainable Development Goals
IEEE Geoscience and Remote Sensing Magazine, Volume: 10, No. 2, June 2022
Preprint: https://arxiv.org/pdf/2112.11367.pdf
https://ieeexplore.ieee.org/document/9681713
F. Nex, C. Armenakis, M. Cramer, D.A. Cucci, M. Gerke, E. Honkavaara, A. Kukko, C. Persello, J. Skaloud
UAV in the advent of the twenties: Where we stand and what is next
ISPRS Journal of Photogrammetry and Remote Sensing, 184 (2022), 215–242
https://www.sciencedirect.com/science/article/pii/S0924271621003282
X. Sun, W. Zhao, R.V. Maretto, C. Persello
Building Polygon Extraction from Aerial Images and Digital Surface Models with a Frame Field Learning Framework
Remote Sensing 2021, Vol. 13, Page 4700, 13(22), p. 4700. doi: 10.3390/RS13224700.
https://www.mdpi.com/2072-4292/13/22/4700
Merodio Gómez, P. et al.
Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images
Sustainability 2021, Vol. 13, Page 12640. Multidisciplinary Digital Publishing Institute, 13(22), p. 12640. doi: 10.3390/SU132212640.
https://www.mdpi.com/2071-1050/13/22/12640
J.R. Bergado, C. Persello, K. Reinke, A. Stein
Predicting Wildfire Burns from Big Geodata using Deep Learning
Safety Science, Volume 140, August 2021.
https://www.sciencedirect.com/science/article/abs/pii/S0925753521001211
A. G. Mullissa, C. Persello and J. Reiche,
Despeckling Polarimetric SAR Data Using a Multistream Complex-Valued Fully Convolutional Network
IEEE Geoscience and Remote Sensing Letters, March 2021.
https://ieeexplore.ieee.org/document/9386223
W. Zhao, C. Persello, A. Stein,
Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 175, 2021, Pages 119-131.
https://www.sciencedirect.com/science/article/pii/S0924271621000551
C.M. Gevaert, C. Persello, R. Sliuzas and G. Vosselman
Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles
International Journal of Applied Earth Observation and Geoinformation, Vol 90, August 2020.
https://www.sciencedirect.com/science/article/pii/S0303243419309900?dgcid=rss_sd_all
K.M. Masoud, C. Persello, and V.A. Tolpekin
Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks
Remote Sens. 2020, 12(1), 59.
https://www.mdpi.com/2072-4292/12/1/59
A. Mullissa, C. Persello, and A. Stein,
PolSARNet: A Deep Fully Convolutional Network for Polarimetric SAR Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
https://ieeexplore.ieee.org/document/8936481
R. Liu, M. Kuffer, and C. Persello
The Temporal Dynamics of Slums Employing a CNN-Based Change Detection Approach
Remote Sensing, 2019, 11(23), 2844.
https://www.mdpi.com/2072-4292/11/23/2844
Z. Zhang, G. Vosselman, M. Gerke, C. Persello, D. Tuia, M.Y. Yang
Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data
Remote Sens. 2019, 11(20), 2417.
https://www.mdpi.com/2072-4292/11/20/2417
X. Xia, C. Persello, and M. Koeva
Deep Fully Convolutional Networks for Cadastral Boundary Detection from UAV Images
Remote Sensing, Vol. 11, No. 14, 2019
https://www.mdpi.com/2072-4292/11/14/1725
C. Persello, V.A. Tolpekin, J.R. Bergado, R.A. de By
Delineation of Agricultural Fields in Smallholder Farms from Satellite Images using Fully Convolutional Networks and Combinatorial Grouping
Remote Sensing of the Environment, Vol. 231, 15 September 2019.
https://www.sciencedirect.com/science/article/pii/S003442571930272X
A. Ajami, M. Kuffer, C. Persello, and K. Pfeffer
Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks
Remote Sensing, Vol. 11, No.11, 2019
https://www.mdpi.com/2072-4292/11/11/1282
H. Tanyas, C.J. van Westen, C. Persello, M. Alvioli
Rapid prediction of the magnitude scale of landslide events triggered by an earthquake
Landslides, January 2019
https://link.springer.com/article/10.1007/s10346-019-01136-4
A. Samat, S. Liu, C. Persello, E. Li, Z. Miao, J.Abuduwaili
Evaluation of ForestPA for VHR RS image classification using spectral and superpixel-guided morphological profiles
European Journal of Remote Sensing, Vol. 52, Issue 1, 2019
https://www.tandfonline.com/doi/full/10.1080/22797254.2019.1565418
A. Rizaldy, C. Persello, C. Gevaert, S. Oude Elberink, G. Vosselman
Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks
Remote Sensing, Vol. 10, No.11, 2018
https://www.mdpi.com/2072-4292/10/11/1723
M. Kuffer, J. Wang, M. Nagenborg, K. Pfeffer, D. Kohli, R. Sliuzas, C. Persello,
The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator
ISPRS International Journal of Geo-Information, 7(11), 428, 2018
https://www.mdpi.com/2220-9964/7/11/428
G. Leonita, M. Kuffer, R. Sliuzas and C. Persello,
Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia
Remote Sensing, Vol. 10, September 2018.
https://www.mdpi.com/2072-4292/10/10/1522
C.M. Gevaert, C. Persello, F. Nex and G. Vosselman,
A deep learning approach to DTM extraction from imagery using rule-based training labels
ISPRS Journal of Photogrammetry and Remote Sensing, vol. 142, August 2018, Pages 106-123.
J.R. Bergado, C. Persello, A. Stein
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 11, pp. 6361-6374, Nov. 2018.
A. Samat, C. Persello, S. Liu, E. Li, Z. Miao and J. Abuduwaili,
Classification of VHR Multispectral Images Using ExtraTrees and Maximally Stable Extremal Region-Guided Morphological Profile
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 9, pp. 3179-3195, Sept. 2018.
C.M. Gevaert, R. Sliuzas, C. Persello, and G. Vosselman,
Evuating the Societal Impact of Using Drones to Support Urban Upgrading Projects
ISPRS International Journal of Geo-Information, Vol. 7, No. 3, 2018.
C. Persello and A. Stein
Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images
IEEE Geoscience and Remote Sensing Letters, Vol 14, No. 12, December 2017.
N. Mboga, C. Persello, J.R. Bergado, A. Stein
Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks
Remote Sensing, Vol. 9, 2017.
C.M. Gevaert, C. Persello, S.J. Oude Elberink, G. Vosselman, R. Sliuzas
Context-based filtering of noisy labels for automatic base-map updating from UAV data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
A. Samat, C. Persello, P. Gamba, S. Liu, J. Abuduwaili and E. Li
Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification
Remote Sensing, Vol. 9, No. 4, 2017.
C.M. Gevaert, C. Persello, R. Sliuzas and G. Vosselman,
Informal settlement classification using point-cloud and image-based features from UAV data
ISPRS Journal of Photogrammetry and Remote Sensing, vol. 125, pp. 225–236, March 2017.
C. M. Gevaert, R.V. Sliuzas, C. Persello, G. Vosselman,
Opportunities for UAV mapping to support unplanned settlement upgrading
Rwanda Journal: Life and Natural Sciences, Series D., no. 4 (open access), 2016
C.M. Gevaert, C. Persello and G. Vosselman,
Optimizing Multiple Kernel Learning for the Classification of UAV Data
Remote Sensing, Vol. 8, No. 12, 2016.
D. Tuia, C. Persello, L. Bruzzone,
Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances
IEEE Geoscience and Remote Sensing Magazine, Vol. 4, No. 2, 2016.
C. Persello and L. Bruzzone,
Kernel-based Domain Invariant Feature Selection in Hyperspectral Images for Transfer Learning
IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 2615-2626, 2016.
C. Persello, A. Boularis, M. Dalponte, T. Gobakken, E. Naesset, and B. Schölkopf,
Cost-sensitive active learning with lookahead: optimizing field surveys for remote sensing data classification,
IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 10, pp. 6652-6664, 2014.
DOI - PDF
C. Persello and L. Bruzzone,
Active and semisupervised learning for the classification of remote sensing images,
IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 6937-6956, 2014.
DOI - PDF
C. Persello,
Interactive domain adaptation for the classification of remote sensing images using active learning,
IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 736–740, 2013.
DOI - PDF
C. Persello and L. Bruzzone,
Active learning for domain adaptation in the supervised classification of remote sensing images,
IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 11, pp. 4468–4483, 2012.
DOI - PDF
B. Demir, C. Persello, and L. Bruzzone,
Batch-mode active-learning methods for the interactive classification of remote sensing images,
IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 3, pp. 1014–1031, 2011.
DOI - PDF
C. Persello and L. Bruzzone,
A novel protocol for accuracy assessment in classification of very high resolution images,
IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 3, pp. 1232–1244, 2010.
DOI - PDF
L. Bruzzone and C. Persello,
A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability,
IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 9, pp. 3180–3191, 2009.
DOI - PDF
L. Bruzzone and C. Persello,
A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples,
IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 9, pp. 3180–3191, 2009.
DOI - PDF
N. Soldati, S. Robinson, C. Persello, J. Jovicich, and L. Bruzzone,
Automatic classification of brain resting states using fMRI temporal signals,
Electronics Letters, vol. 45, no. 1, pp. 19–21, 2009.
Book Chapters
K. Zhang, B. Schölkopf, K. Muandet, Z. Wang, Z. Zhou, and C. Persello,
Single-source domain adaptation with target and conditional shift,
in Regularization, Optimization, Kernels, and Support Vector Machines, CRC Press, 2014.
L. Bruzzone, C. Persello, and B. Demir,
Active learning methods in classification of remote sensing images,
in Signal and Image Processing for Remote Sensing, C. Chen, Ed., 2nd, CRC Press - Taylor and Francis, 2012, ch. 15, pp. 303–323.
L. Bruzzone and C. Persello,
Approaches based on support vector machine to classification of remote sensing data,
in Handbook of Pattern Recognition and Computer Vision, C. Chen, Ed., World Scientific, 2009, ch. 3.2, pp. 329–352.
International Conferences
NOT UPDATED
C. Danilla, C. Persello, V. Tolpekin, J.R. Bergado,
Classification of Multitemporal SAR Images using Convolutional Neural Networks and Markov Random Fields
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2017.
N. Mboga, C. Persello, J.R. Bergado, A. Stein,
Detection of Informal Settlements from VHR Satellite Images using Convolutional Neural Networks
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2017.
C.M. Gevaert, C. Persello, S.J. Oude Elberink, G. Vosselman, and R.V. Sliuzas,
An automated technique for basemap updating using UAV data
In: Proceedings of Joint urban remote sensing event, JURSE 2017.
Sliuzas, R.V., Kuffer, M., Pfeffer, K., Gevaert, C.M. and Persello, C.,
Slum mapping: from space to unmanned aerial vehicle based approaches
In: Proceedings of Joint urban remote sensing event, JURSE 2017.
C.M. Gevaert, C. Persello, R.V. Sliuzas, G. Vosselman
Classification of informal settlements through the integration of 2D and 3D features extracted from UAV data
in Proceedings of the XXIII ISPRS Congress, Peer reviewed Annals, 2016.
Z. Xu, C. Persello, M. Li, L. Wu, and P. Wu
Two-level active learning method for debris detection using VHR satellite imagery and local aerial surveys
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2016.
J.R. Bergado, C. Persello, C. Gevaert
A deep learning approach to the classification of sub-decimetre resolution aerial images
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2016.
C.M. Gevaert, C. Persello, R.V. Sliuzas, G. Vosselman
Integration of 2D and 3D features from UAV imagery for informal settlement classification using Multiple Kernel Learning
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2016.
Gevaert, C.M., Sliuzas, R.V., Persello, C. and Vosselman, G.,
Opportunities for UAV mapping to support unplanned settlement upgrading.
In: Proceedings of GeoTech Rwanda 2015, Kigali, Rwanda, 18-20 November 2015.
C. Persello and L. Bruzzone,
Relevant and invariant feature selection of hyperspectral images for domain generalization
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2014.
C. Persello, M. Dalponte, T. Gobakken, and E. Naesset,
Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2013.
C. Persello and F. Dinuzzo,
Interactive domain adaptation technique for the classification of remote sensing images,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2012.
C. Persello and L. Bruzzone,
A novel active learning strategy for domain adaptation in the classification of remote sensing images,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2011.
C. Persello and L. Bruzzone,
Active versus semi-supervised learning paradigm for the classification of remote sensing images,
in SPIE Remote Sensing, 2011.
L. Bruzzone and C. Persello,
Recent trends in classification of remote sensing data: active and semisupervised machine learning paradigms,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2010.
L. Bruzzone and C. Persello,
Active learning for classification of remote sensing images,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2009.
C. Persello and L. Bruzzone,
A novel approach to the selection of spatially invariant features for classification of hyperspectral images,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2009.
L. Bruzzone and C. Persello,
A novel approach to the selection of robust and invariant features for classification of hyperspectral images,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2008.
L. Bruzzone and C. Persello,
A novel protocol for accuracy assessment in classification of very high resolution multispectral and sar images,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2008.
L. Bruzzone, M. Marconcini, and C. Persello,
Fusion of spectral and spatial information by a novel SVM classification technique,
in IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2007.
National Conferences
E. Arnoldi, L. Bruzzone, L. Carlin, L. Pedron, and C. Persello,
Sistema avanzato per la classificazione delle aree agricole in immagini ad elevata risoluzione geometrica,
in Conferenza Nazionale ASITA 2007, 2007.
National Journals
E. Arnoldi, L. Bruzzone, L. Carlin, L. Pedron, and C. Persello,
Classificazione di immagini telerilevate satellitari per agricoltura di precisione,
MondoGis: il mondo dei sistemi informativi geografici: rivista bimestrale di informazione tecnica, vol. 63, pp. 13–17, 2007.