The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Multiple Feature Learning for Hyperspectral Image Classification
Abstract
Abstract—Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through nonlinear transformations intended to reduce data dimensionality, to better model the inherent nonlinearity of the original data (e.g., kernels) or to adequately exploit the spatial information contained in the scene (e.g., using morphological analysis). Special attention has been given to techniques able to exploit a single kind of features, such as composite kernel learning or multiple kernel learning, developed in order to deal with multiple kernels. However, few
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Cites
Hyperspectral remote sensing data analysis and future challenges
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223
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Cited by
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Self-citationHe, L., Li, J., Liu, C., & Li, S. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2765364 · Cited by 630
Self-citationZabalza, J., Ren, J., Zheng, J., Zhao, H., Qing, C., Yang, Z., Du, P., & Marshall, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.11.044 · Cited by 389
Learning multiscale and deep representations for classifying remotely sensed imagery
Zhao, W., & Du, S. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.004 · Cited by 398
Hyperspectral Image Classification via Multiple-Feature-Based Adaptive Sparse Representation
Fang, L., Wang, C., Li, S., & Benediktsson, J. A. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2664480 · Cited by 172
Self-citationClassification of Hyperspectral Images by Gabor Filtering Based Deep Network
Kang, X., Li, C., Li, S., & Lin, H. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2767185 · Cited by 151
Metadata
- Authors
- Li, J., Huang, X., Gamba, P., Bioucas-Dias, J. M. B., Zhang, L., Benediktsson, J. A., & Plaza, A.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2015-03
- Volume
- 53
- Issue
- 3
- Pages
- 1592-1606
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2041100636
- Cited by
- 311
- References
- 1
- FWCI
- 58.78
- Type
- journal article
- Language
- en
- Open access
- closed
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