The DOI matches its Crossref record.
Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions
Abstract
Mixed pixels occur commonly in remotely-sensed imagery, especially those with a coarse spatial resolution. They are a problem in land-cover mapping applications since image classification routines assume ‘pure’ or homogeneous pixels. By unmixing a pixel into its component parts it is possible to enableinter alia more accurate estimation of the areal extent of different land cover classes. In this paper two approaches to estimating sub-pixel land cover composition are investigated. One is a linear mixture model the other is a regression model based on fuzzy membership functions. For both approaches significant correlation coefficients, all >0·7, between the actual and predicted proportion of a land cover type within a pixel were obtained. Additionally a case study is presented in which the accuracy of the estimation of tropical forest extent is increased significantly through the use of sub-pixel estimates of land-cover composition rather than a conventional image classification.
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Citations
Cited by
Keshava, N., & Mustard, J. F. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974727 · Cited by 2,549
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
Metadata
- Authors
- FOODY, G. M., & COX, D. P.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 1994-02
- Volume
- 15
- Issue
- 3
- Pages
- 619-631
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2267362924
- Cited by
- 353
- References
- 0
- FWCI
- 44.09
- Type
- journal article
- Language
- en
- Open access
- closed
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