Bayesian fusion of hyperspectral and multispectral images - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Bayesian fusion of hyperspectral and multispectral images


This paper presents a Bayesian fusion technique for multi-band images. 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 related to the linear mixing model for hyperspectral images is introduced. To compute Bayesian estimators of the scene of interest from its posterior distribution, a Gibbs sampling algorithm is proposed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimensional distribution, a Hamiltonian Monte Carlo step is introduced in this Gibbs sampler. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques.
Fichier principal
Vignette du fichier
Wei_12915.pdf (819.78 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01150337 , version 1 (11-05-2015)


  • HAL Id : hal-01150337 , version 1
  • OATAO : 12915


Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret. Bayesian fusion of hyperspectral and multispectral images. IEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2014, May 2014, Florence, Italy. pp. 3176-3180. ⟨hal-01150337⟩
112 View
262 Download


Gmail Facebook X LinkedIn More