Remote sensing; Machine learning; Multitemporal image analysis; Statistical models; Disaster managment; Landscape genetics
Camps-Valls Gustavo, Tuia Devis, Bruzzone Lorenzo, Benediktsson Jon Atli (2014), Advances in Hyperspectral Image Classification, in IEEE SIGNAL PROCESSING MAGAZINE
, 31(1), 45-54.
Grazioli Jacopo, Tuia Devis, Monhart S., Schneebeli Marc, Raupach Timothy H., Berne Alexis (2014), Hydrometeor classification from two-dimensional video disdrometer data, in Atmospheric Measurement Techniques
, 7(9), 2869-2882.
Laparra Valero, Jiménez Sandra, Tuia Devis, Camps-Valls Gustau (2014), PRINCIPAL POLYNOMIAL ANALYSIS, in International Journal of Neural Systems
, 24(7), 1440007.
Huo Lianzhi, Tang Ping, Zhang Zheng, Tuia Devis (2014), Semisupervised classification of remote sensing images with hierarchical spatial similarity, in IEEE Geoscience and Remote Sensing Letters
, 12(1), 150-154.
Volpi Michele, Matasci Giona, Kanevski Mikhail F., Tuia Devis (2014), Semi-supervised multiview embedding for hyperspectral data classification, in Neurocomputing
, 145, 427-437.
Pasolli Edoardo, Melgani Farid, Tuia Devis, Pacifici Fabio, Emery William J. (2014), SVM active learning approach for image classification using spatial information, in IEEE Transactions on Geoscience and Remote Sensing
, 52(4), 2217-2223.
Crawford Melba M., Tuia Devis, Yang Hsiuhan Lexie (2013), Active Learning: Any Value for Classification of Remotely Sensed Data?, in PROCEEDINGS OF THE IEEE
, 101(3), 593-608.
Tuia Devis, Volpi Michele, Mura Mauro Dalla, Rakotomamonjy Alain, Flamary Rémi (2013), Automatic feature learning for spatio-spectral image classification with sparse SVM, in IEEE Transactions on Geoscience and Remote Sensing
, 52(10), 6062-6074.
Tuia Devis, Muñoz-Marí Jordi, Rojo-Álvarez Jose´ Luis, Martínez-Ramón Manel A., Camps-Valls Gustavo (2013), Explicit recursive and adaptive filtering in reproducing kernel hilbert spaces, in IEEE Transactions on Neural Networks and Learning Systems
, 25(7), 1413-1419.
Tuia Devis, Merényi Erzsébet, Jia Xiuping, Grana-Romay Manuel (2013), Foreword to the special issue on machine learning for remote sensing data processing, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, 7(4), 1007-1011.
Tuia Devis, Munoz-Mari Jordi, Gomez-Chova Luis, Malo Jesus (2013), Graph Matching for Adaptation in Remote Sensing, in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 51(1), 329-341.
Tuia Devis, Munoz-Mari Jordi (2013), Learning User's Confidence for Active Learning, in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 51(2), 872-880.
Tuia Devis, Volpi Michele, Trolliet Maxime, Camps-Valls Gustavo (2013), Semisupervised manifold alignment of multimodal remote sensing images, in IEEE Transactions on Geoscience and Remote Sensing
, 52(12), 7708-7720.
de Morsier Frank, Tuia Devis, Borgeaud Maurice, Gass Volker, Thiran Jean-Philippe (2013), Semi-Supervised Novelty Detection Using SVM Entire Solution Path, in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 51(4), 1939-1950.
Volpi M, Tuia D, Bovolo F, Kanevski M, Bruzzone L (2013), Supervised change detection in VHR images using contextual information and support vector machines, in INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
, 20, 77-85.
Flamary R, Tuia D, Labbe B, Camps-Valls G, Rakotomamonjy A (2012), Large Margin Filtering, in IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 60(2), 648-659.
Volpi M, Tuia D, Kanevski M (2012), Memory-Based Cluster Sampling for Remote Sensing Image Classification, in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 50(8), 3096-3106.
Longbotham N, Pacifici F, Glenn T, Zare A, Volpi M, Tuia D, Christophe E, Michel J, Inglada J, Chanussot J, Du Q (2012), Multi-Modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 Data Fusion Contest, in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
, 5(1), 331-342.
Kalbermatten M, Van De Ville D, Turberg P, Tuia D, Joost S (2012), Multiscale analysis of geomorphological and geological features in high resolution digital elevation models using the wavelet transform, in GEOMORPHOLOGY
, 138(1), 352-363.
Tuia D, Munoz-Mari J, Camps-Valls G (2012), Remote sensing image segmentation by active queries, in PATTERN RECOGNITION
, 45(6), 2180-2192.
Munoz-Mari J, Tuia D, Camps-Valls G (2012), Semisupervised Classification of Remote Sensing Images With Active Queries, in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 50(10), 3751-3763.
Matasci G, Tuia D, Kanevski M (2012), SVM-Based Boosting of Active Learning Strategies for Efficient Domain Adaptation, in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
, 5(5), 1335-1343.
Volpi M, Tuia D, Camps-Valls G, Kanevski M (2012), Unsupervised Change Detection With Kernels, in IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
, 9(6), 1026-1030.
De Morsier Frank, Tuia Devis, Borgeaud Maurice, Gass Volker, Thiran Jean Philippe, Cluster validity measure and merging system for hierarchical clustering considering outliers, in Pattern Recognition
In this project, I propose to develop theoretical solutions to practical limitations in remote sensing data analysis.I propose to study the underlying structure of high resolution remote sensing data, to characterize its nonlinearities and to study the variations of this structure when acquisition conditions change. This way, it will be possible to develop adaptable classification models that can process images of different zones, taken at different times and by different sensors, thus filling a major gap in current remote sensing research and meeting the endusers expectations. The project aims at creating models that can be applied to several images, thus allowing to surpass an important limitaiton to the use of remote sensing images in real world applications. To ensure this last point, the project also aims at developing validated applicative tools for applications needing landuse maps or environmental parameters retrieved from remote sensing data. To this end, real case studies in landscape genetics, disaster management and atmospheric modeling will be considered.Summarizing, the project will participate in scientific advances in the fields of machine learning and fill theoretical gaps in current remote sensing image processing research that prevent the field to meet users' expectations.