Estimation of the Hyperspectral Tucker ranks
| Autor: | Alexis Huck, 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) |
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
0211 other engineering and technologies
Image processing 02 engineering and technology 01 natural sciences Matrix decomposition Data cube Statistics::Machine Learning [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing I.5.4 Signal Processing Computer vision Linear combination 021101 geological & geomatics engineering Mathematics Ranks Spectral signature Pixel business.industry 010401 analytical chemistry Hyperspectral imaging Unmixing 0104 chemical sciences Tucker Hyperspectral Tensor Computer Science::Computer Vision and Pattern Recognition Artificial intelligence business Algorithm [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Tucker decomposition |
| Zdroj: | Proceeding of IEEE International conference on Acoustics, Speech and Signal Processing 2009 IEEE International conference Acoustics, Speech and Signal Processing 2009 ICASSP 2009 IEEE International conference Acoustics, Speech and Signal Processing 2009 ICASSP 2009, Apr 2009, Tapei, Taiwan. pp.1281-1284, ⟨10.1109/ICASSP.2009.4959825⟩ ICASSP |
| DOI: | 10.1109/ICASSP.2009.4959825⟩ |
| Popis: | In hyperspectral image analysis, one often assumes that observed pixel spectra are linear combinations of pure substance spectra. Unmixing a hyperspectral image consists in finding the number of pure substances in the scene, finding their spectral signatures and estimating the abundance fraction of each pure substance spectrum in each spectral pixel. In this paper, we show that the tensor Tucker decomposition could be considered to solve this problem, and a preliminary problem to overcome consists in estimating the 3 required data Tucker ranks, corresponding to the 3 dimensions of the data cube. Then, we propose an optimal method to estimate them. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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