Publications

GO TO : Home - Project - Publications - Talks - Code - Contacts

Manifold adaptation

• D. Tuia, M. Volpi, M. Trolliet, G. Camps-Valls. Semisupervised manifold alignment of multimodal remote sensing images, IEEE Trans. Geosci. Remote Sens., 52(12):7708-7720, 2014. [code]
• V. Laparra, S. Jimenez, D. Tuia. Principal polynomial analysis, Int. J. Neural Systems, 24(7), 2014.
• M. Volpi, G. Matasci, M. Kanevski, D. Tuia. Semi-supervised multiview embedding for hyperspectral data classification. Neurocomputing, 145:427–437, 2014. 
• D. Tuia, J. Muñoz-Marí, L. Gómez-Chova, and J. Malo. Graph matching for adaptation in remote sensing. IEEE Trans. Geosci. Remote Sens. , 51(1): 329-341, 2013.

Active learning

• M. Crawford, D. Tuia  L. H. Yang. Active learning: any value for classificatio of remotely sensed data?. Proceeding of the IEEE, 101(3): 593-608, 2013.
• D. Tuia and J. Muñoz-Marí. Learning user’s confidence for active learning. IEEE Trans. Geosci. Remote Sens., 51(2): 872-880, 2013.
• E. Pasolli, F. Melgani, D. Tuia, F. Pacifici and W. J. Emery. SVM active learning using spatial information. IEEE Trans. Geosci. Remote Sens., 52(4): 2217-2233, 2014.
• J. Muñoz-Marí, D. Tuia, and G. Camps-Valls. Semisupervised classification of remote sensing images with active queries. IEEE Trans. Geosci. Remote Sens., 50(10): 3751-3763, 2012. 
• G. Matasci, D. Tuia, and M. Kanevski. SVM-based boosting of active learning strategies for efficient domain adaptation. IEEE J. Sel. Topics Appl. Earth Observ., 5(5): 1335-1343, 2012. 
• D. Tuia, E. Pasolli, W.J. Emery. Using active learning to adapt remote sensing classifiers, Remote Sensing of Environment, 115(9): 2232-2242, 2011.

Change detection

• F. De Morsier, D. Tuia, M. Borgeaud, V. Gass, J. P. Thiran, Semisupervised novelty detection using SVM entire solution path. IEEE Trans. Geosci. Remote Sens., 51(4): 1939-1950, 2013.
• N. Longbotham, F. Pacifici, T. Glenn, A. Zare, M. Volpi, D. Tuia, E. Christophe, J. Michel, J. Inglada, J. Chanussot, and Q. Du. Multi-modal change detection, application to the detection of flooded areas: outcome of the 2009-2010 data fusion contest. IEEE J. Sel. Topics Appl. Earth Observ., 5(1):331–342, 2012. 
• M. Volpi, D. Tuia, G. Camps-Valls, and M. Kanevski. Unsupervised change detection with kernels. IEEE Geosci. Remote Sens. Lett., 9(6):1026–1030, 2012. 
• M. Volpi, D. Tuia, F. Bovolo, M. Kanevski, L. Bruzzone. Supervised change detection in VHR images using contextual information and support vector machines, Int. J. Appli. Earth Obs. Geoinf., 20: 77-85, 2013.

General

• G. Camps-Valls, D. Tuia, L. Bruzzone, J.A. Benediktsson. Advances in hyperspectral image classification, IEEE Sig. Proc. Mag., 31:45-54, 2014.
• D. Tuia, M. Volpi, M. Dalla Mura, A. Rakotomamonjy, R. Flamary. Automatic feature learning for spatio-spectral image classification with sparse SVM, IEEE Trans. Geosci. Remote Sens., 52(10): 6062-6074, 2014.
• F. De Morsier, D. Tuia, M. Borgeaud, V. Gass, J.-Ph. Thiran. Cluster validity measure and merging system for hierarchical clustering considering outliers. Pattern Recogn., in press. 

Note concerning publications: This material is presented to ensure timely dissemination of scholarly and technical work related to the SNSF project PZ00P2_136827. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be  reposted without the explicit permission of the copyright holder.

top