The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Hyperspectral Image Classification Using Deep Pixel-Pair Features
Abstract
The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.
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Cites
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
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
Spectral-spatial classification of hyperspectral data based on deep belief network
Chen, Y., Zhao, X., & Jia, X. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2388577 · Cited by 1,259 · cited 1×
“In [21], spectral–spatial features were obtained via deep belief network and the classification procedure was implemented by logistic regression.”
Method
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
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
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Spatial-Spectral Transformer for Hyperspectral Image Classification
He, X., Chen, Y., & Lin, Z. (2021) · Remote Sensing · doi:10.3390/rs13030498 · Cited by 462
Metadata
- Authors
- Li, W., Wu, G., Zhang, F., & Du, Q.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2017-02
- Volume
- 55
- Issue
- 2
- Pages
- 844-853
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2548791488
- Cited by
- 805
- References
- 3
- FWCI
- 81.05
- Type
- journal article
- Language
- en
- Open access
- closed
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