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A generalized confusion matrix for assessing area estimates from remotely sensed data
Abstract
The formulation of a generalized area-based confusion matrix for exploring the accuracy of area estimates is presented. The generalized confusion matrix is appropriate for both traditional classification algorithms and sub-pixel area estimation models. An error matrix, derived from the generalized confusion matrix, allows the accuracy of maps generated using area estimation models to be assessed quantitatively and compared to the accuracies obtained from traditional classification techniques. The application of this approach is demonstrated for an area estimation model applied to Landsat data of an urban area of the United Kingdom.
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Cited by
Hong, H., Branham, W. S., Ng, H. W., Moland, C. L., Dial, S. L., Fang, H., Perkins, R., Sheehan, D., & Tong, W. (2015) · Toxicological Sciences · doi:10.1093/toxsci/kfu231 · Cited by 85
Qu, R., Xu, G., Ding, C., Jia, W., & Sun, M. (2020) · IEEE Access · doi:10.1109/access.2020.2991845 · Cited by 59
Metadata
- Authors
- Lewis, H. G., & Brown, M.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2001-01
- Volume
- 22
- Issue
- 16
- Pages
- 3223-3235
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2170186417
- Cited by
- 236
- References
- 0
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A generalized confusion matrix for assessing area estimates from remotely sensed data. https://app.cochat.ai/papers/10.1080/01431160152558332
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- Lewis, H. G., & Brown, M. (2001). A generalized confusion matrix for assessing area estimates from remotely sensed data. International Journal of Remote Sensing. https://doi.org/10.1080/01431160152558332
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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).
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