The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Sample size determination for image classification accuracy assessment and comparison
Foody, G. M. (2009) · International Journal of Remote Sensing · doi:10.1080/01431160903130937 · Cited by 248
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AID: A benchmark data set for performance evaluation of aerial scene classification
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2685945 · Cited by 2,372
Deep learning in environmental remote sensing: Achievements and challenges
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., et al. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111716 · Cited by 1,697
Land-cover classification with high-resolution remote sensing images using transferable deep models
Tong, X.-Y., Xia, G.-S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111322 · Cited by 978
Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B., & Li, Z. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.02.045 · Cited by 540
Why Cohen’s Kappa should be avoided as performance measure in classification
Delgado, R., & Tibau, X.-A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0222916 · Cited by 324
Machine learning algorithms for social media analysis: A survey
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100395 · Cited by 276
Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning
Folberth, C., Baklanov, A., Balkovič, J., Skalský, R., Khabarov, N., & Obersteiner, M. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.09.021 · Cited by 161
“Within agricultural sciences, applications are to date mostly limited to processing 114 and analyzes of remote sensing data (e.g. Duro et al., 2012; Ali et al., 2015).”
BackgroundXu, S., & Pan, Z. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104283 · Cited by 87
Zhang, C., Liu, Y., & Tie, N. (2023) · Forests · doi:10.3390/f14020254 · Cited by 42
Metadata
- Authors
- Duro, D. C., Franklin, S. E., & Dubé, M. G.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 118
- Pages
- 259-272
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2082081125
- Cited by
- 989
- References
- 5
- FWCI
- 55.62
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
