The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Pixel-wise regression using U-Net and its application on pansharpening
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
“Models like U-Net [58] and DeepLab [59] can be used for pixel-wise classification, which is useful in highly dense and occluded environments when individuals need to be segmented precisely and distinguish people from backgrounds at a pixel level.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [58]
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,266
Receptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685
Cited by
Sorted by citations
Sun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 585
Vivone, G., Mura, M. D., Garzelli, A., Restaino, R., Scarpa, G., Ulfarsson, M. O., Alparone, L., & Chanussot, J. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3019315 · Cited by 364
Rundo, L., Han, C., Nagano, Y., Zhang, J., Hataya, R., Militello, C., Tangherloni, A., Nobile, M. S., Ferretti, C., Besozzi, D., et al. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.006 · Cited by 290
Machine Learning in Pansharpening: A benchmark, from shallow to deep networks
Deng, L.-J., Vivone, G., Paoletti, M. E., Scarpa, G., He, J., Zhang, Y., Chanussot, J., & Plaza, A. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3187652 · Cited by 260
Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks
Hu, J., Huang, T., Deng, L.-J., Jiang, T.-X., Vivone, G., & Chanussot, J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084682 · Cited by 241
Metadata
- Authors
- Yao, W., Zeng, Z., Lian, C., & Tang, H.
- Affiliations
- South Central Minzu University (CN)
- Huazhong University of Science and Technology (CN)
- Wuhan University of Technology (CN)
- China University of Geosciences (CN)
- Corresponding author
- Zeng, Zhigang
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 312
- Pages
- 364-371
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2807567802
- Cited by
- 133
- References
- 38
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 12 · 2023: 20 · 2022: 20 · 2021: 26 · 2020: 21 · 2019: 16
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- Natural Science Foundation of China (61673188, 61503144, 61761130081)
- Natural Science Foundation of Hubei Province (2017CFB519)
- Topics
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- 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
- Keywords
- pansharpening, U-Net, convolutional neural networks, remote sensing images, multi-resolution features, geometric deformation, color distortion, image fusion
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
