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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Schramm, M. Landmann, T. Schmidt, M. Dech, S. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Geogr., Univ. of Wurzburg, Wurzburg (Landmann, T.) || German Remote Sensing Data Center, German Aerosp. Center, Oberpfaffenhofen (Schmidt, M.; Dech, S.) || Inst. of Photogrammetry & Geolnformation, Leibniz Univ. of Hannover, Hannover (Schramm, M.) |
| Abstract | Detection of subpixel endmember proportions in the realm of land cover and land cover change mapping is imperative when considering new and innovative methods to improve local scale land over estimates. However, often these results are not accurate enough due to rare endmember spectra information. Several studies including spectral unmixing without known target spectra exist, however, the models used herein are based on the assumption that all endmembers extracted from imagery and employed are spectrally `pure'. But in medium resolution satellite data especially in semi arid regions for instance pure photosynthesis active vegetation pixels are rare, so these approaches seems to be unsuitable. In this paper, we present a new spectral unmixing model that does not need exact a priori knowledge about endmember spectra. A best fitting function between the endmember types, shadow' and, green vegetation' and the land cover type, tree' is calculated. As an experimental result, a comparison between tree density derived from this approach based on a 15 m resolution ASTER dataset and a 1 m resolution IKONOS classification is shown to illustrate the accuracy of the model. |
| File Size | 487821 |
| File Format | |
| ISBN | 9781424428076 |
| DOI | 10.1109/IGARSS.2008.4779345 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-07-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Equations Vegetation mapping Libraries Remote sensing Satellites Geography Data mining Classification tree analysis Linear systems Least squares methods tree density linear spectral unmixing unknown endmember |
| Content Type | Text |
| Resource Type | Article |
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