Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization

Nonlinear spectral mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel optimization method of nonlinear unmixing based on a generalized bilinear model (GBM), which considers the second-order scattering of photons in a spectr...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 52; číslo 2; s. 1430 - 1437
Hlavní autori:	Yokoya, Naoto, Chanussot, Jocelyn, Iwasaki, Akira
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.02.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Abundance Accuracy Agricultural land Applied geophysics Complexity theory Computational efficiency Earth sciences Earth, ocean, space Engineering Sciences Exact sciences and technology Extraction Factorization Generalized bilinear model (GBM) Hyperspectral imaging Image processing Internal geophysics Mixture models nonlinear unmixing Nonlinearity Optimization Optimization methods Robustness semi-nonnegative matrix factorization Signal and Image processing Soil Spectra Synthetic data Vegetation mapping image processing models spectra cost maps optimization Generalized bilinear model (GBM) accuracy extraction nonlinear unmixing errors semi-nonnegative matrix factorization
ISSN:	0196-2892, 1558-0644
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