The DOI matches its Crossref record; 137 of the 138 references it cites were found in a registry or an index.
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For medium-resolution and low-resolution satellite image data, in particular, there is a lack of benchmark datasets, while several highresolution benchmark datasets exist (PatternNet, UCMD, aerial image dataset) (Zhou et al., 2018).”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Zhou et al., 2018
Experimental — accuracy not yet measured
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Cited by
Sorted by citations
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
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2918242 · Cited by 1,951
Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities
Zhang, L., & Zhang, L. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3145854 · Cited by 647
Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding
Sümbül, G., Charfuelàn, M., Demir, B., & Markl, V. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8900532 · Cited by 623
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
Liu, F., Chen, D., Guan, Z., Zhou, X., Zhu, J., Ye, Q., Fu, L., & Zhou, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3390838 · Cited by 507
Metadata
- Authors
- Zhou, W., Newsam, S., Li, C., & Shao, Z.
- Affiliations
- Wuhan University (CN)
- University of California, Merced (US)
- Corresponding author
- Shao, Zhenfeng
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 145
- Pages
- 197-209
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2626107033
- Cited by
- 571
- References
- 138
- Citations by year
- 2026: 64 · 2025: 79 · 2024: 78 · 2023: 86 · 2022: 74 · 2021: 65 · 2020: 61 · 2019: 44 · 2018: 19
- FWCI
- 35.81
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Technologies Research and Development Program (2016YFB0502603)
- Fundamental Research Funds for the Central Universities (2042016kf0179, 2042016kf1019, 2016070204010114)
- Wuhan Chen Guang Project
- Guangzhou science and technology project (201604020070)
- Special task of technical innovation in Hubei Province (2016AAA018)
- National Natural Science Foundation of China (61671332, 41771452, 41771454)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- remote sensing image retrieval, benchmark dataset, high-resolution imagery, land use and land cover classification, deep learning, performance evaluation, background noise
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
