Fast unsupervised extraction of endmembers spectra from hyperspectral data
Linear unmixing decomposes an hyperspectral image into a collection of re ectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixture...
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| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2016
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| Texto completo: | http://hdl.handle.net/10400.21/6151 |
| País: | Portugal |
| Oai: | oai:repositorio.ipl.pt:10400.21/6151 |
| Resumo: | Linear unmixing decomposes an hyperspectral image into a collection of re ectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art methods with much lower computational complexity. |
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