The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Convolutional neural networks for hyperspectral image classification
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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
Human Parsing with Contextualized Convolutional Neural Network
Liang, X., Xu, C., Shen, X., Yang, J., Liu, S., Tang, J., Lin, L., & Yan, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.163 · Cited by 245
Self-citation
Cited by
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
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
(2019) · ACM Computing Surveys · doi:10.1145/3230633 · Cited by 303
Eling, M., Nuessle, D., & Staubli, J. (2022) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-020-00201-7 · Cited by 156
Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
Shadman Roodposhti, M., Aryal, J., Lucieer, A., & Bryan, B. A. (2019) · Entropy · doi:10.3390/e21010078 · Cited by 65
Metadata
- Authors
- Yu, S., Jia, S., & Xu, C.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 219
- Pages
- 88-98
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2519653196
- Cited by
- 703
- References
- 7
- FWCI
- 59.07
- Type
- journal article
- Language
- en
- Open access
- green
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: Convolutional neural networks for hyperspectral image classification. https://app.cochat.ai/papers/10.1016/j.neucom.2016.09.010
- The paper
- Yu, S., Jia, S., & Xu, C. (2017). Convolutional neural networks for hyperspectral image classification. Neurocomputing. https://doi.org/10.1016/j.neucom.2016.09.010
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.
