Welcome...

C. Paris PhD (Claudia)

Assistant Professor

About Me

I am an Assistant Professor in the Department of Natural Resources, Faculty of Geo-information Science and Earth Observation. I received my Ph.D in Information and Communication Technology from the University of Trento, Italy, in 2016. In 2014, I was a visiting PhD student at the Rochester Institute of Technology (RIT), Rochester, New York State, USA, working on the fusion of airborne and terrestrial LiDAR data. In 2016, I was a visiting Post-Doc at the Instituto Superior Técnico, Lisbon, Portugal, working on the superresolution of multiresolution multispectral remote sensing images. My research concentrates mainly on passive and active remote sensing and, in particular, on designing novel and automatic system architecture for large-scale environmental monitoring. My main research interests include remote sensing image processing, signal processing and pattern recognition with specific reference to classification and fusion of multisource remote sensing data (LiDAR data, hyperspectral, multispectral and high resolution optical images), multi-temporal image analysis, domain-adaptation methods and deep-learning models. Moreover, my research interests are also focused on the use of remote sensing data for sustainable development.

Expertise

Engineering & Materials Science
Fusion Reactions
Optical Radar
Remote Sensing
Satellites
Time Series
Triangulation
Earth & Environmental Sciences
Detection
Learning

Publications

Recent
Sedona, R. , Paris, C., Cavallaro, G., Bruzzone, L., & Riedel, M. (2021). A high-performance multispectral adaptation GAN for harmonizing dense time series of Landsat-8 and Sentinel-2 images. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 10134 - 10146. https://doi.org/10.1109/jstars.2021.3115604
Weikmann, G. , Paris, C., & Bruzzone, L. (2021). TimeSen2Crop: A million labeled samples dataset of Sentinel 2 image time series for crop-type classification. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 4699-4708. [9408357]. https://doi.org/10.1109/JSTARS.2021.3073965
Hagos, D. H., Kakantousis, T., Vlassov, V., Sheikholeslami, S., Wang, T., Dowling, J. , Paris, C., Marinelli, D., Weikmann, G., Bruzzone, L., Khaleghian, S., Krmer, T., Eltoft, T., Marinoni, A., Pantazi, D-A., Stamoulis, G., Bilidas, D., Papadakis, G., Mandilaras, G., ... Cziferszky, A. (2021). ExtremeEarth meets satellite data from space. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 9038-9063. https://doi.org/10.1109/JSTARS.2021.3107982
Paris, C., Weikmann, G., & Bruzzone, L. (2020). A study of the robustness of the long short-term memory classifier to cloudy time series of multispectral images. 1-10. Paper presented at Image and signal processing for remote sensing XXVI. https://doi.org/10.1117/12.2574383
Podsialdo, I. , Paris, C., Callegari, M., Marin, C., Gunter, D., Strasser, U., Notarnicola, C., & Bruzzone, L. (2020). Integrating models and remote sensing data for distributed glacier mass balance estimation. IEEE Journal of selected topics in applied earth observations and remote sensing, 6177-6194. https://doi.org/10.1109/jstars.2020.3028653
Paris, C., & Bruzzone, L. (2020). A novel approach to the unsupervised extraction of reliable training samples from thematic products. IEEE transactions on geoscience and remote sensing, 59(3), 1930-1948. [9121728]. https://doi.org/10.1109/tgrs.2020.3001004
Harikumar, A. , Paris, C., Bovolo, F., & Bruzzone, L. (2020). A crown quantization-based approach to tree-species classification using high-density airborne laser scanning data. IEEE transactions on geoscience and remote sensing, 59(5), 4444-4453. https://doi.org/10.1109/tgrs.2020.3012343
Gregorio, L. D., Bovolo, F., Callegari, M., Günther, D., Marin, C., Niroumand-Jadidi, M. , Paris, C., Podsiadlo, I., Strasser, U., Zebisch, M., Bruzzone, L., & Notarnicola, C. (2020). Snow Parameters Estimation Through New Data Fusion Approaches Involving a Hydrological Model and Remote Sensing Products. 1. Abstract from International Conference on Snow Hydrology., Bolzano, Italy. https://snowhydro.eurac.edu/
Paris, C., Bioucas-Dias, J., & Bruzzone, L. (2019). A Novel Sharpening Approach for Superresolving Multiresolution Optical Images. IEEE transactions on geoscience and remote sensing, 57(3), 1545-1560. [8472286]. https://doi.org/10.1109/TGRS.2018.2867284
Paris, C., & Bruzzone, L. (2019). A Growth-Model-Driven Technique for Tree Stem Diameter Estimation by Using Airborne LiDAR Data. IEEE transactions on geoscience and remote sensing, 57(1), 76-92. [8428490]. https://doi.org/10.1109/TGRS.2018.2852364
Podsiadlo, I. , Paris, C., Bovolo, F., Callegari, M., De Gregorio, L., Günther, D., Marin, C., Marke, T., Niroumand-Jadidi, M., Notarnicola, C., Strasser, U., Zebisch, M., & Bruzzone, L. (2019). Integration of hydro-climatological model and remote sensing for glacier mass balance estimation. Paper presented at SPIE Remote Sensing 2019, Strasbourg, France. https://doi.org/10.1117/12.2533232
Koubarakis, M., Bereta, K., Bilidas, D., Giannousis, K., Ioannidis, T., Pantazi, D-A., Stamoulis, G., Haridi, S., Vlassov, V., Bruzzone, L. , Paris, C., Eltoft, T., Krämer, T., Charalabidis, A., Karkaletsis, V., Konstantopoulos, S., Dowling, J., Kakantousis, T., Datcu, M., ... Fleming, A. (2019). From copernicus big data to extreme earth analytics. In EDBT/ICDT 2019 Joint Conference (pp. 690-693). [321] https://doi.org/10.5441/002/edbt.2019.88
Bovolo, F., Bruzzone, L., Fernández-Prieto, D. , Paris, C., Solano-Correa, Y. T., Volden, E., & Zanetti, M. (2019). Big Data from Space for Precision Agriculture Applications. In S. Nativi, C. Wang, G. Landgraf, M. A. Liberti, P. Mazzetti, & Z. S. Mohamed-Ghouse (Eds.), 11th International Symposium on Digital Earth (ISDE 11) (Vol. 509, pp. 1-3). [012004] IOP. https://doi.org/10.1088%2F1755-1315%2F509%2F1%2F012004
Paris, C., & Bruzzone, L. (2019). Automatic Extraction of Weak Labeled Samples From Existing Thematic Products For Training Convolutional Neural Networks. In 2019 IEEE International Geoscience & Remote Sensing Symposium: Proceedings (pp. 5722-5725). [8900649] IEEE. https://doi.org/10.1109/IGARSS.2019.8900649
Marinelli, D. , Paris, C., & Bruzzone, L. (2019). An Automatic Technique for Deciduous Trees Detection in High Density Lidar Data Based on Delaunay Triangulation. In 2019 IEEE International Geoscience & Remote Sensing Symposium: Proceedings (pp. 94-97). [8899772] IEEE. https://doi.org/10.1109/IGARSS.2019.8899772
Marinelli, D. , Paris, C., & Bruzzone, L. (2019). An Approach to Tree Detection Based on the Fusion of Multitemporal LiDAR Data. IEEE geoscience and remote sensing letters, 16(11), 1771-1775. [8698891]. https://doi.org/10.1109/LGRS.2019.2908314
Bertoluzza, M. , Paris, C., & Bruzzone, L. (2019). A Fast Method for Cloud Removal and Image Restoration on Time Series of Multispectral Images. In 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) [8866920] IEEE. https://doi.org/10.1109/Multi-Temp.2019.8866920
Paris, C., Bruzzone, L., & Diego, F-P. (2019). A Novel Approach to the Unsupervised Update of Land-Cover Maps by Classification of Time Series of Multispectral Images. IEEE transactions on geoscience and remote sensing, 57(7), 4259-4277. [8635553]. https://doi.org/10.1109/TGRS.2018.2890404
Paris, C., & Bruzzone, L. (2018). A Sensor-Driven Hierarchical Method for Domain Adaptation in Classification of Remote Sensing Images. IEEE transactions on geoscience and remote sensing, 56(3), 1308-1324. [8088346]. https://doi.org/10.1109/TGRS.2017.2761839
Marinelli, D. , Paris, C., & Bruzzone, L. (2018). Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds. In 2018 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 3999-4002). [8518441] IEEE. https://doi.org/10.1109/IGARSS.2018.8518441
Harikumar, A. , Paris, C., Bovolo, F., & Bruzzone, L. (2018). A novel data-driven approach to tree species classification using high density multireturn airborne lidar data. In L. Bruzzone, & F. Bovolo (Eds.), Image and Signal Processing for Remote Sensing XXIV (Vol. XXIV). [107890E] SPIE. https://doi.org/10.1117/12.2325634
Paris, C., Bruzzone, L., & Fernandez-Prieto, D. (2018). A Novel Method Based on Source Domain Understanding and Modeling to Transfer Labels from Land-Cover Vector Maps to Classifiers for Multispectral Images. In 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, understanding and forecasting the dynamics of our planet (pp. 3619-3622). [8517458] IEEE. https://doi.org/10.1109/IGARSS.2018.8517458
Marinelli, D. , Paris, C., & Bruzzone, L. (2018). A Novel Approach to 3-D Change Detection in Multitemporal LiDAR Data Acquired in Forest Areas. IEEE transactions on geoscience and remote sensing, 56(6), 3030-3046. [8272508]. https://doi.org/10.1109/TGRS.2018.2789660

UT Research Information System

Google Scholar Link

Affiliated Study Programmes

Master

Contact Details

Visiting Address

University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands

Navigate to location

Mailing Address

University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands