The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers
Abstract
In this letter, an alternative data-driven HSI classification model based on CapsNet is proposed rather than recently predominant convolutional neural network (CNN)-based models. To adjust the CapsNet to HSI classification, we tune a new CapsNet architecture with three convolutional layers. The added shallow layer provides higher level features to the primary capsules, which indirectly speeds up the following routing procedure. To guarantee a good convergence of the whole CapsNet, the three shallow layers are initialized by transferring convolutional parameters from a pretrained CNN model. The improved CapsNet-based models with and without vote strategy both achieve significantly superior performance in HSI classification to the state-of-the-art CNN-based methods on real hyperspectral data sets.
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“We used parameters of a pre-trained model to initialize ST-CapsNet in attention layers and two convolution layers to obtain better convergence and avoid local optimum as suggested in [35].”
From ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement (2023) · cites this paper as [35]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Research works printed but not linked: 19
- 10.1109/LGRS.2017.2774253 · DOI
- 10.1109/TIP.2017.2725580 · DOI
- 10.1109/CVPR.2015.7298594 · DOI · Paper page
- 10.1109/LGRS.2017.2786272 · DOI
- 10.1109/TGRS.2016.2616355 · DOI
- 10.1109/LGRS.2017.2704625 · DOI
- sabour, 2017, Dynamic routing between capsules, Proc Conf Adv Neural Inf Process Syst (NIPS), 3856
- xi, 2017, Capsule Network Performance on Complex Data[J]
- 10.1109/TGRS.2017.2698503 · DOI
- 10.1109/TGRS.2002.804721 · DOI
- 10.1109/TGRS.2008.2005729 · DOI
- 10.1109/TGRS.2004.831865 · DOI
- jia, 2015, Gabor feature-based collaborative representation for hyperspectral imagery classification, IEEE Trans Geosci Remote Sens, 53, 1118, 10.1109/TGRS.2014.2334608 · DOI
- 10.1109/TGRS.2004.842481 · DOI
- 10.1016/j.rse.2012.08.029 · DOI
- 10.1109/TGRS.2016.2584107 · DOI
- 10.1109/TGRS.2017.2686444 · DOI
- 10.1109/TGRS.2011.2129595 · DOI
- 2018, DataOnline
Cited by
Wang, Z., Chen, C., Li, J., Wan, F., Sun, Y., & Wang, H. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3237319 · Cited by 75
“Sabour et al. suggested that better convergence can be obtained by using three routing iterations than one iteration [35].”
BackgroundMethodCapsule Network for ERP Detection in Brain-Computer Interface
Ma, R., Yu, T., Zhong, X., Yu, Z. L., Li, Y., & Gu, Z. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3070327 · Cited by 47
Metadata
- Authors
- Yin, J., Li, S., Zhu, H., & Luo, X.
- Affiliations
- Beihang University (CN)
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2019-07
- Volume
- 16
- Issue
- 7
- Pages
- 1095-1099
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2911968856
- Cited by
- 58
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 11 · 2022: 11 · 2021: 15 · 2020: 11 · 2019: 2
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Geoscience and Remote Sensing Letters (publishedVersion)
- Funders
- National Natural Science Foundation of China (41471278, 41871240)
- Civil Aviation Administration of China (U1833117)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- hyperspectral image classification, capsule networks, capsule routing, feature extraction
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
