Minimum Dispersion Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data
| Autor: | Alexis Huck, Jacques Blanc-Talon, Mireille Guillaume |
|---|---|
| Přispěvatelé: | Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Mission pour la Recherche et l'Innovation Scientifique (DGA/MRIS), Ministère de la défense, DGA : délégation générale de l'armement |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Endmember
Projected Gradient 0211 other engineering and technologies 02 engineering and technology Blind signal separation Non-negative matrix factorization Matrix decomposition Statistics::Machine Learning [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing I.5.4 Signal Processing 0202 electrical engineering electronic engineering information engineering Source separation Non-negative Matrix Factorization (NMF) Electrical and Electronic Engineering 021101 geological & geomatics engineering Mathematics Pixel business.industry Hyperspectral imaging Pattern recognition Hyperspectral unmixing regularization Computer Science::Computer Vision and Pattern Recognition Blind source separation General Earth and Planetary Sciences A priori and a posteriori 020201 artificial intelligence & image processing Artificial intelligence business Algorithm [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Spectral unmixing Regularization function |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, 2010, PP (99), pp.1-13. ⟨10.1109/TGRS.2009.2038483⟩ IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2010, PP (99), pp.1-13. ⟨10.1109/TGRS.2009.2038483⟩ IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2010, 48 (6), pp.2590-2600. ⟨10.1109/TGRS.2009.2038483⟩ IEEE Transactions on Geoscience and Remote Sensing, 2010, 48 (6), pp.2590-2600. ⟨10.1109/TGRS.2009.2038483⟩ |
| ISSN: | 0196-2892 |
| DOI: | 10.1109/TGRS.2009.2038483⟩ |
| Popis: | International audience; This paper considers the problem of unsupervised spectral unmixing for hyperspectral image analysis. Each observed pixel is assumed to be a noisy linear mixture of pure material spectra, namely endmembers. The mixing coefficients, usually called abundances, are constrained to positive and summed to unity. The proposed unmixing approach is based on the Non-negative Matrix Factorization (NMF) framework, which considers the physical constraints of the problem, including the positivity of the endmember spectra and abundances. However, the basic NMF formulation has degenerated solutions and suffers from non-convexity limitations.We consider here a regularization function, called dispersion, which favours the solution such thatthe endmember spectra have minimum variances. Such a solution encourages the recovered spectra to be flat, preserving thepossible spectral singularities (peaks and sharp variations). The regularized criterion is minimized with a Projected Gradient (PG) scheme and we propose a new step-size estimation technique to fasten the PG convergence. The derived algorithm is called MiniDisCo, for Minimum Dispersion Constrained NMF. We experimentally compare MiniDisCo with recently proposed algorithms. It is shown to be particularly robust to the presence of flat spectra, to a possible a priori overestimation of the number of endmembers or if the amount of observed spectral pixels is low. In addition, experiments show that the considered regularization correctly overcomes the degeneracy and nonconvexity problems, leading to satisfactory unmixing accuracy. We include a comparative analysis of a real world scene. |
| Databáze: | OpenAIRE |
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