Published 2012
| Version v1
Conference paper
Nonlinear unmixing of hyperspectral images based on multi-kernel learning
- Creators
- Chen, Jie
- Richard, Cédric
- Honeine, Paul
- Others:
- Joseph Louis LAGRANGE (LAGRANGE) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)
- Laboratoire Modélisation et Sûreté des Systèmes (LM2S) ; Institut Charles Delaunay (ICD) ; Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)
Description
Nonlinear unmixing of hyperspectral images has generated considerable interest among researchers, as it may overcome some inherent limitations of the linear mixing model. In this paper, we formulate the problem of estimating abundances of a nonlinear mixture of hyperspectral data based on a new multi-kernel learning paradigm. Experiments are conducted using both synthetic and real images in order to illustrate the effectiveness of the proposed method.
Abstract
International audience
Additional details
- URL
- https://hal.archives-ouvertes.fr/hal-01966012
- URN
- urn:oai:HAL:hal-01966012v1
- Origin repository
- UNICA