The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Support vector machines for classification in remote sensing
Abstract
Support vector machines (SVM) represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of two experiments in which multi‐class SVMs are compared with maximum likelihood (ML) and artificial neural network (ANN) methods in terms of classification accuracy. The two land cover classification experiments use multispectral (Landsat‐7 ETM+) and hyperspectral (DAIS) data, respectively, for test areas in eastern England and central Spain. Our results show that the SVM achieves a higher level of classification accuracy than either the ML or the ANN classifier, and that the SVM can be used with small training datasets and high‐dimensional data.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Cited by
Support vector machines in remote sensing: a review
Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473
The application of artificial neural networks to the analysis of remotely sensed data
Mas, J. F., & Flores, J. J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701352154 · Cited by 616
Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014) · European Journal of Remote Sensing · doi:10.5721/eujrs20144723 · Cited by 538
Deep support vector machine for hyperspectral image classification
Okwuashi, O., & Ndehedehe, C. E. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107298 · Cited by 283
A patch-based convolutional neural network for remote sensing image classification
Sharma, A., Liu, X., Yang, X., & Shi, D. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.07.017 · Cited by 260
Metadata
- Authors
- Pal, M., & Mather, P. M.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2005-03
- Volume
- 26
- Issue
- 5
- Pages
- 1007-1011
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2065800647
- Cited by
- 1,090
- References
- 3
- FWCI
- 42.73
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
