The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach
Abstract
In this paper, we propose a spectral–spatial feature based classification (SSFC) framework that jointly uses dimension reduction and deep learning techniques for spectral and spatial feature extraction, respectively. In this framework, a balanced local discriminant embedding algorithm is proposed for spectral feature extraction from high-dimensional hyperspectral data sets. In the meantime, convolutional neural network is utilized to automatically find spatial-related features at high levels. Then, the fusion feature is extracted by stacking spectral and spatial features together. Finally, the multiple-feature-based classifier is trained for image classification. Experimental results on well-known hyperspectral data sets show that the proposed SSFC method outperforms other commonly used methods for hyperspectral image classification.
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Cites
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
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
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Cited by
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
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
Deep Learning for Hyperspectral Image Classification: An Overview
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007
Convolutional neural networks for large-scale remote-sensing image classification
Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2612821 · Cited by 1,114
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983
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
Metadata
- Authors
- Zhao, W., & Du, S.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2016-08
- Volume
- 54
- Issue
- 8
- Pages
- 4544-4554
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2314785379
- Cited by
- 1,212
- References
- 3
- FWCI
- 123.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhao, W., & Du, S. (2016). Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2016.2543748
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