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Fuzzy classification of remote sensing images
Abstract
A fuzzy supervised classification method in which geographical information is represented as fuzzy sets is described. The algorithm consists of two major steps: the estimate of fuzzy parameters from fuzzy training data, and a fuzzy partition of spectral space. Partial membership of pixels allows component cover classes of mixed pixels to be identified and more accurate statistical parameters to be generated, resulting in a higher classification accuracy. Results of classifying a Landsat MSS image are presented, and their accuracy is analyzed.>
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Cited by
A Review of Nonlinear Hyperspectral Unmixing Methods
Heylen, R., Parente, M., & Gader, P. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2320576 · Cited by 591
Neupane, B., Horanont, T., & Aryal, J. (2021) · Remote Sensing · doi:10.3390/rs13040808 · Cited by 247
There and Back Again: Revisiting Archaeological Locational Modeling
Kvamme, K. (2005) · GIS and Archaeological Site Location Modeling · doi:10.1201/9780203563359.sec1 · Cited by 17
Metadata
- Authors
- Wang, F.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 1990-03
- Volume
- 28
- Issue
- 2
- Pages
- 194-201
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W1648556314
- Cited by
- 472
- References
- 0
- FWCI
- 13.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Fuzzy classification of remote sensing images. https://app.cochat.ai/papers/10.1109/36.46698
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- Wang, F. (1990). Fuzzy classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/36.46698
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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.
