Unsupervised component reduction of hyperspectral images and clustering without performance loss: Application to marine algae identification

B. Chen; K. Chehdi; E. de Oliveira; Claude Cariou; B. Charbonnier

doi:10.1117/12.2241190

Conference papers

Unsupervised component reduction of hyperspectral images and clustering without performance loss: Application to marine algae identification

B. Chen ¹ K. Chehdi ¹ E. de Oliveira ² Claude Cariou ¹ B. Charbonnier ²

1 IETR - Institut d'Électronique et des Technologies du numéRique

2 EDF R&D STEP - Simulation et Traitement de l'information pour l'Exploitation des systèmes de Production

Keywords : Identification (control systems) Independent component analysis Iterative methods Pixels Reduction Remote sensing Signal processing Spectroscopy Feature reduction Classification (of information) Algae stable Hyper-spectral imageries Marine algae unsupervised Image processing

Document type :

Conference papers

Domain :

Computer Science [cs] / Networking and Internet Architecture [cs.NI]

Engineering Sciences [physics] / Electronics

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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01484540
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, March 7, 2017 - 1:22:20 PM
Last modification on : Tuesday, October 6, 2020 - 3:09:45 AM

Unsupervised component reduction of hyperspectral images and clustering without performance loss: Application to marine algae identification

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Unsupervised component reduction of hyperspectral images and clustering without performance loss: Application to marine algae identification

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