期刊
REMOTE SENSING
卷 12, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/rs12142326
关键词
hyperspectral imaging; spectral unmixing; sparse unmixing; endmember variability
类别
资金
- EU FP7 through the ERANETMED JC-WATER program
- MapInvPlnt Project [ANR-15-NMED-0002-02]
- MUESLI IDEX ATS project
- Toulouse INP
- European Research Council [ERC FACTORY-CoG-681839]
- ANR-3IA Artificial and Natural Intelligence Toulouse Institute (ANITI)
Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.
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