Parameter Estimation For Blind lq Hyperspectral Unmixing Using Bayesian Optimization

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.