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.
CITATION STYLE
Figliuzzi, B., Velasco-Forero, S., Bilodeau, M., & Angulo, J. (2016). A Bayesian approach to linear unmixing in the presence of highly mixed spectra. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10016 LNCS, pp. 263–274). Springer Verlag. https://doi.org/10.1007/978-3-319-48680-2_24
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