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Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions
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“Early studies concerning LULC classification based on DL mostly focused on feature representation or learning, while the final classification used other simpler classifiers (Tuia et al., 2015).”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Tuia et al., 2015
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Remote Sensing and Image Interpretation
Wright, J., LILLESAND, T. M., & Kiefer, R. W. (1980) · Geographical Journal · doi:10.2307/634969 · Cited by 7,281
Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261
Support vector machines in remote sensing: a review
Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335
Remote sensing and image interpretation
Cook, E. A. (1995) · Preventive Veterinary Medicine · doi:10.1016/0167-5877(95)90036-5 · Cited by 3,330
Remote Sensing Digital Image Analysis
Richards, J. A., & Jia, X. (2005) · Springer eBooks · doi:10.1007/3-540-29711-1 · Cited by 2,768
Recent advances in techniques for hyperspectral image processing
Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps‐Valls, G., Chanussot, J., Fauvel, M., Gamba, P. E., Gualtieri, A. G., et al. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.028 · Cited by 1,723
Classification of hyperspectral data from urban areas based on extended morphological profiles
Benediktsson, J. A., Palmason, J. A., & Sveinsson, J. R. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.842478 · Cited by 1,426
Advances in Spectral-Spatial Classification of Hyperspectral Images
Fauvel, M., Tarabalka, Y., Benediktsson, J. A., Chanussot, J., & Tilton, J. C. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2197589 · Cited by 1,288
Deep Learning of Representations for Unsupervised and Transfer Learning.
Bengio, Y. (2011) · Cited by 1,080
Foody, G. M. (2004) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.70.5.627 · Cited by 1,065
Selective Logging in the Brazilian Amazon
Asner, G. P., Knapp, D., Broadbent, E. N., Oliveira, P. J. C., Keller, M., & Silva, J. N. M. (2005) · Science · doi:10.1126/science.1118051 · Cited by 1,051
Optimization for Machine Learning
Sra, S., Nowozin, S., & Wright, S. J. (2011) · The MIT Press eBooks · doi:10.7551/mitpress/8996.001.0001 · Cited by 902
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods
Camps‐Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2279179 · Cited by 786
Self-citationSVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
Tarabalka, Y., Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2010) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2010.2047711 · Cited by 763
Morphological Attribute Profiles for the Analysis of Very High Resolution Images
Mura, M. D., Benediktsson, J. A., Waske, B., & Bruzzone, L. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2048116 · Cited by 735
Monitoring urbanization in mega cities from space
Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., & Dech, S. W. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.09.015 · Cited by 467
Pacifici, F., Chini, M., & Emery, W. J. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.02.014 · Cited by 385
Mapping abandoned agriculture with multi-temporal MODIS satellite data
Alcántara, C., Kuemmerle, T., Prishchepov, A. V., & Radeloff, V. C. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.05.019 · Cited by 337
Grafting: fast, incremental feature selection by gradient descent in function space
Perkins, S. J., Lacker, K., & Theiler, J. P. (2003) · doi:10.5555/944919.944976 · Cited by 310
Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data
Laurin, G. V., Chen, Q., Lindsell, J., Coomes, D. A., Frate, F. D., Guerriero, L., Pirotti, F., & Valentini, R. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.01.001 · Cited by 301
Convex Optimization with Sparsity-Inducing Norms
Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2011) · The MIT Press eBooks · doi:10.7551/mitpress/8996.003.0004 · Cited by 299
Naidoo, L., Cho, M. A., Mathieu, R., & Asner, G. P. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2012.03.005 · Cited by 287
Moser, G., Serpico, S. B., & Benediktsson, J. A. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2211551 · Cited by 250
Learning Relevant Image Features With Multiple-Kernel Classification
Tuia, D., Camps‐Valls, G., Matasci, G., & Kanevski, M. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2049496 · Cited by 219
Self-citationAn Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification
Konrad Schindler, B. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2192741 · Cited by 194
Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification
Li, W., & Du, Q. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2295313 · Cited by 192
Tuia, D., Pacifici, F., Kanevski, M., & Emery, W. J. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2027895 · Cited by 182
Self-citationActive Learning: Any Value for Classification of Remotely Sensed Data?
Crawford, M. M., Tuia, D., & Yang, H. L. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2231951 · Cited by 176
Self-citationRemote Sensing Image Processing
Camps‐Valls, G., Tuia, D., Gómez‐Chova, L., Jiménez, S., & Malo, J. (2012) · Synthesis lectures on image, video, and multimedia processing · doi:10.1007/978-3-031-02247-0 · Cited by 140
Self-citationConsideration of smoothing techniques for hyperspectral remote sensing
Vaiphasa, C. (2005) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2005.11.002 · Cited by 109
Remote Sensing Image Processing
Camps-Valls, G., Tuia, D., Gómez‐Chova, L., Jiménez, S., & Malo, J. (2011) · Synthesis lectures on image, video, and multimedia processing · doi:10.2200/s00392ed1v01y201107ivm012 · Cited by 100
Self-citation$\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning
Rakotomamonjy, A., Flamary, R., Gasso, G., & Canu, S. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2157521 · Cited by 90
Self-citationAutomatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM
Tuia, D., Volpi, M., Mura, M. D., Rakotomamonjy, A., & Flamary, R. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2294724 · Cited by 82
Self-citationDelineation of Central Business Districts in mega city regions using remotely sensed data
Taubenböck, H., Klotz, M., Wurm, M., Schmieder, J., Wagner, B., Wooster, M. J., Esch, T., & Dech, S. W. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.05.019 · Cited by 79
Multitask Remote Sensing Data Classification
Leiva-Murillo, J. M., Gómez‐Chova, L., & Camps‐Valls, G. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2200043 · Cited by 65
Pacifici, F., Du, Q., & Prasad, S. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2277532 · Cited by 23
Optimization with Sparsity-Inducing Penalties (Foundations and Trends(R) in Machine Learning)
Bach, F., Jenatton, R., & Mairal, J. (2011) · now publishers, Inc. eBooks · Cited by 20
Learning with infinitely many features
Rakotomamonjy, A., Flamary, R., & Yger, F. (2012) · Machine Learning · doi:10.1007/s10994-012-5324-5 · Cited by 13
Self-citationReport on the 2013 IEEE GRSS Data Fusion Contest: Fusion of Hyperspectral and LiDAR Data
Pacifici, F. (2013) · Cited by 4
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Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
Self-citationDeep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Zhang, L., Zhang, L., & Du, B. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2540798 · Cited by 2,272
Deep learning classifiers for hyperspectral imaging: A review
Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.09.006 · Cited by 1,147
Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?
Penatti, O. A. B., Nogueira, K., & dos Santos, J. A. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301382 · Cited by 740
Metadata
- Authors
- Tuia, D., Flamary, R., & Courty, N.
- Affiliations
- University of Zurich (CH)
- Centre National de la Recherche Scientifique (FR)
- Université de Bretagne Sud (FR)
- Corresponding author
- Tuia, Devis
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 105
- Pages
- 272-285
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2063102607
- Cited by
- 102
- References
- 45
- Citations by year
- 2026: 4 · 2025: 1 · 2023: 3 · 2022: 3 · 2021: 9 · 2020: 15 · 2019: 17 · 2018: 13 · 2017: 15 · 2016: 15 · 2015: 7
- FWCI
- 24.90
- Type
- journal article
- Language
- en
- Open access
- green
- research.wur.nl
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- arXiv (Cornell University)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Swiss National Science Foundation (PP00P2_150593)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- hyperspectral image classification, sparse feature learning, hierarchical feature learning, nonlinear classification, urban land cover classification, multimodal data
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