The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification
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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
“Inspired by the progress achieved in DL-based image super-resolution, many DL based remote sensing image fusion methods have been proposed in the last few years, indicating the distinct advantages over traditional methods owing to the better capability of DL models in characterizing the complex relationship between the input and the target images (Liu et al., 2018).”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Liu et al., 2018
In the citing paper
“In other words, a systematic and comprehensive review concerning the application of DL in the field of remote sensing has not yet been conducted.”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Liu et al., 2018
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
(2010) · International Conference on Machine Learning · Cited by 13,995
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,703
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
A comparison of methods for multiclass support vector machines
Hsu, C.-W., & Lin, C.-J. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,838
Research works printed but not linked: 2
- Gao, 2008, A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions, Online J. Earth Sci., 2, 27
- Torrey, 2009, Transfer learning, Handbook Res. Mach. Learn. Appl. Trends: Algorithms, Methods, Techniques, 1, 242
Cited by
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
Metadata
- Authors
- Liu, T., & Abd-Elrahman, A.
- Affiliations
- University of Florida (US)
- Corresponding author
- Abd-Elrahman, Amr
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 139
- Pages
- 154-170
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2794055043
- Cited by
- 103
- References
- 78
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 13 · 2022: 11 · 2021: 20 · 2020: 23 · 2019: 12 · 2018: 8
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Funders
- U.S. Army Corps of Engineers
- 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
- Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
- Keywords
- deep convolutional neural networks, object-based image analysis, training data enrichment, land cover classification, wetland classification, orthophotos, voting classification, limited training data
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.
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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.
