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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 52; no. 2; pp. 1430 - 1437
Main Authors: Yokoya, Naoto, Chanussot, Jocelyn, Iwasaki, Akira
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.02.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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 spectral mixture model. Semi-nonnegative matrix factorization (semi-NMF) is used for the optimization to process a whole image in matrix form. When endmember spectra are given, the optimization of abundance and interaction abundance fractions converge to a local optimum by alternating update rules with simple implementation. The proposed method is evaluated using synthetic datasets considering its robustness for the accuracy of endmember extraction and spectral complexity, and shows smaller errors in abundance fractions rather than conventional methods. GBM-based unmixing using semi-NMF is applied to the analysis of an airborne hyperspectral image taken over an agricultural field with many endmembers, and it visualizes the impact of a nonlinear interaction on abundance maps at reasonable computational cost.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2013.2251349