The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification
Abstract
It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.
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Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,550
Daugman, J. G. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.001160 · Cited by 3,219
Advances in Large-Margin Classifiers
(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,869
Composite Kernels for Hyperspectral Image Classification
Camps-Valls, G., Gomez-Chova, L., Munoz-Mari, J., Vila-Frances, J., & Calpe-Maravilla, J. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.857031 · Cited by 1,068
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
Fauvel, M., Benediktsson, J. A., Chanussot, J., & Sveinsson, J. R. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.922034 · Cited by 990
Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles
Fauvel, M., Chanussot, J., Benediktsson, J. A., & Sveinsson, J. R. (2007) · 2007 IEEE International Geoscience and Remote Sensing Symposium · doi:10.1109/igarss.2007.4423943 · Cited by 904
Rotation invariant texture classification using LBP variance (LBPV) with global matching
Guo, Z., Zhang, L., & Zhang, D. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2009.08.017 · Cited by 800
Dominant Local Binary Patterns for Texture Classification
Liao, S., Law, M. W. K., & Chung, A. C. S. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2015682 · Cited by 785
Cited by
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 1,073
Multisource Remote Sensing Data Classification Based on Convolutional Neural Network
Xu, X., Li, W., Ran, Q., Du, Q., Gao, L., & Zhang, B. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2756851 · Cited by 689
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
A review on extreme learning machine
Wang, J., Lu, S., Wang, S.-H., & Zhang, Y.-D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11007-7 · Cited by 572
Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
Wan, S., Gong, C., Zhong, P., Du, B., Zhang, L., & Yang, J. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2949180 · Cited by 553
Metadata
- Authors
- Li, W., Chen, C., Su, H., & Du, Q.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2015-07
- Volume
- 53
- Issue
- 7
- Pages
- 3681-3693
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2152057649
- Cited by
- 685
- References
- 38
- FWCI
- 109.21
- Type
- journal article
- Language
- en
- Open access
- closed
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