Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent method

Overview & Synopsis

This paper studies a new Bayesian optimization algorithm for fusing hyperspectral and multispectral images. The hyperspectral image is supposed to be obtained by blurring and subsampling a high spatial and high spectral target image. The multispectral image is modeled as a spectral mixing version of the target image. By introducing appropriate priors for parameters and hyperparameters, the fusion problem is formulated within a Bayesian estimation frame-work, which is very convenient to model the noise and the target image. The high spatial resolution hyperspectral image is then inferred from its posterior distribution. To compute the Bayesian maximum a posteriori estimator associated with this posterior, an alternating direction method of multipliers within block coordinate descent algorithm is proposed. Simulation results demonstrate the efficiency of the proposed fusion method when compared with several state-of-the-art fusion techniques.