The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Implementation of machine-learning classification in remote sensing: an applied review
Open accessAbstract
Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.
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Cites
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Cited by
Phan, T. N., Kuch, V., & Lehnert, L. W. (2020) · Remote Sensing · doi:10.3390/rs12152411 · Cited by 616
A. Ramezan, C., A. Warner, T., & E. Maxwell, A. (2019) · Remote Sensing · doi:10.3390/rs11020185 · Cited by 322
Okolie, C. J., & Smit, J. L. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.03.016 · Cited by 139
Land-surface parameters for spatial predictive mapping and modeling
Maxwell, A. E., & Shobe, C. M. (2022) · Earth-Science Reviews · doi:10.1016/j.earscirev.2022.103944 · Cited by 105
Self-citationCondran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022) · IEEE Access · doi:10.1109/access.2022.3188649 · Cited by 99
Metadata
- Authors
- Maxwell, A. E., Warner, T. A., & Fang, F.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2018-02-02
- Volume
- 39
- Issue
- 9
- Pages
- 2784-2817
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2793927960
- Cited by
- 2,057
- References
- 11
- FWCI
- 100.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
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