A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra
| Autor: | Santiago Velasco-Forero, Jesús Angulo, Michel Bilodeau, Bruno Figliuzzi |
|---|---|
| Přispěvatelé: | Centre de Morphologie Mathématique (CMM), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Jacques Blanc-Talon, Cosimo Distante, Wilfried Philips, Dan Popescu, Paul Scheunders |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Endmember
Abundance estimation business.industry Computer science Bayesian probability 0211 other engineering and technologies 020206 networking & telecommunications 02 engineering and technology linear mixing model Machine learning computer.software_genre Spectral line Mixing (mathematics) [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Simulated annealing 0202 electrical engineering electronic engineering information engineering Source separation Bayesian algorithm Artificial intelligence business computer Algorithm 021101 geological & geomatics engineering |
| Zdroj: | Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016 Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Oct 2016, Leecy, Italy. pp.263--274, ⟨10.1007/978-3-319-48680-2_24⟩ Advanced Concepts for Intelligent Vision Systems ISBN: 9783319486796 ACIVS |
| DOI: | 10.1007/978-3-319-48680-2_24⟩ |
| Popis: | International audience; In this article, we present a Bayesian algorithm for endmember extraction and abundance estimation in situations where prior information is available for the abundances. The algorithm is considered within the framework of the linear mixing model. The novelty of this work lies in the introduction of bound parameters which allow us to introduce prior information on the abundances. The estimation of these bound parameters is performed using a simulated annealing algorithm. The algorithm is illustrated by simulations conducted on synthetic AVIRIS spectra and on the SAMSON dataset. |
| Databáze: | OpenAIRE |
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