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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers. https://app.cochat.ai/papers/10.1109/lgrs.2019.2891076
- The paper
- Yin, J., Li, S., Zhu, H., & Luo, X. (2019). Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2019.2891076
Data sources
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.
