The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Open accessAbstract
Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593
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
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
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 7×
“applied unsupervised deep feature learning—including stacked autoencoder (SAE) [28] and deep brief network (DBN) [31]—for spectral–spatial feature extraction and classification.”
Influential
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
Generative Adversarial Networks for Hyperspectral Image Classification
Zhu, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2805286 · Cited by 734
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
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, Y., Zhang, H., & Shen, Q.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2017-01-13
- Volume
- 9
- Issue
- 1
- Pages
- 67
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2572303978
- Cited by
- 1,320
- References
- 6
- FWCI
- 73.80
- Type
- journal article
- Language
- en
- Open access
- gold
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