HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Contributor : Michel Bilodeau Connect in order to contact the contributor
Submitted on : Tuesday, January 3, 2017 - 6:35:51 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:13 PM



Bruno Figliuzzi, Michel Bilodeau, Jesus Angulo, Santiago Velasco-Forero. A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra. 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⟩. ⟨hal-01425738⟩



Record views