Abstract
Hyperspectral data unmixing has attracted considerable attention in recent years. Hyperspectral data may however suffer from varying levels of signal-to-noise ratio over spectral bands. In this paper, we investigate a robust approach for nonlinear hyperspectral data unmixing. Each observed pixel is modeled as a linear mixing of endmember spectra with nonlinear fluctuations embedded in a reproducing kernel Hilbert space. Welsch M-estimator is considered for reducing the sensitivity of the unmixing process. Experimental results, with both synthetic and real data, illustrate the effectiveness of the proposed scheme.
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