Bayesian Fusion of Multi-Band Images
This work addresses image fusion for remote sensing applications, but it is incremental as it builds on existing Bayesian and Monte Carlo methods with specific algorithmic improvements.
The paper tackles the problem of fusing low-resolution multi-band images to produce high-resolution hyperspectral images by proposing a Bayesian fusion technique with a Hamiltonian Monte Carlo step. The method is evaluated against state-of-the-art techniques, showing efficiency in generating high spatial resolution hyperspectral images from multispectral and hyperspectral inputs.
In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques. In particular, low spatial resolution hyperspectral and multispectral images are fused to produce a high spatial resolution hyperspectral image.