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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| Published in: | IEEE transactions on geoscience and remote sensing Vol. 52; no. 2; pp. 1430 - 1437 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York, NY
IEEE
01.02.2014
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Iwasaki, Akira Chanussot, Jocelyn Yokoya, Naoto |
| Author_xml | – sequence: 1 givenname: Naoto surname: Yokoya fullname: Yokoya, Naoto email: yokoya@sal.rcast.u-tokyo.ac.jp organization: Department of Aeronautics and Astronautics, University of Tokyo, Tokyo, Japan – sequence: 2 givenname: Jocelyn surname: Chanussot fullname: Chanussot, Jocelyn email: jocelyn.chanussot@gipsa-lab.grenoble-inp.fr organization: Grenoble Institute of Technology, Saint Martin d'Hères cedex, France – sequence: 3 givenname: Akira surname: Iwasaki fullname: Iwasaki, Akira email: aiwasaki@sal.rcast.u-tokyo.ac.jp organization: Department of Aeronautics and Astronautics, University of Tokyo, Tokyo, Japan |
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| Keywords | image processing models spectra cost maps optimization Generalized bilinear model (GBM) accuracy extraction nonlinear unmixing errors semi-nonnegative matrix factorization |
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| SubjectTerms | 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 |
| Title | Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization |
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