Bayesian fusion of hyperspectral and multispectral images

Abstract : 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.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01150337
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Monday, May 11, 2015 - 9:14:24 AM
Last modification on : Thursday, October 24, 2019 - 2:44:10 PM
Long-term archiving on: Monday, September 14, 2015 - 9:42:18 PM

File

Wei_12915.pdf
Files produced by the author(s)

Identifiers

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

Collections

Citation

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⟩

Share

Metrics

Record views

141

Files downloads

311