TY - GEN
T1 - Parameter Estimation for Blind lq Hyperspectral Unmixing Using Bayesian Optimization
AU - Sigurdsson, Jakob
AU - Ulfarsson, Magnus O.
AU - Sveinsson, Johannes R.
N1 - Funding Information: This work was supported by the Postdoctoral Fund of the University of Iceland and the Research Fund of the University of Iceland Publisher Copyright: © 2018 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - Blind hyperspectral unmixing is the task of estimating both the pure material spectra (endmembers), and the abundances in hyperspectral images. The performance of hyperspectral unmixing methods is very often dependent on tuning parameters. Accurately estimating these parameters is computationally intensive and this can severely limit the complexity of the underlying model. In this paper, we propose using Bayesian optimization to estimate tuning parameters for blind hyperspectral unmixing. Using real data, we show that the proposed method can be successfully applied to estimate tuning parameters for hyperspectral unmixing. Also, we show that increasing the number of tuning parameters can improve the unmixing results.
AB - Blind hyperspectral unmixing is the task of estimating both the pure material spectra (endmembers), and the abundances in hyperspectral images. The performance of hyperspectral unmixing methods is very often dependent on tuning parameters. Accurately estimating these parameters is computationally intensive and this can severely limit the complexity of the underlying model. In this paper, we propose using Bayesian optimization to estimate tuning parameters for blind hyperspectral unmixing. Using real data, we show that the proposed method can be successfully applied to estimate tuning parameters for hyperspectral unmixing. Also, we show that increasing the number of tuning parameters can improve the unmixing results.
KW - Bayesian optimization
KW - Hyperspectral unmixing
KW - lq regularization
KW - parameter estimation
UR - https://www.scopus.com/pages/publications/85073895857
U2 - 10.1109/WHISPERS.2018.8747247
DO - 10.1109/WHISPERS.2018.8747247
M3 - Conference contribution
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
PB - IEEE Computer Society
T2 - 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018
Y2 - 23 September 2018 through 26 September 2018
ER -