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Article Dans Une Revue IEEE Journal of Selected Topics in Signal Processing Année : 2015

Bayesian Fusion of Multi-Band Images

Résumé

This paper presents a Bayesian fusion technique for remotely sensed 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 exploiting geometrical considerations 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 within a Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques.
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Dates et versions

hal-01187286 , version 1 (26-08-2015)

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Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret. Bayesian Fusion of Multi-Band Images. IEEE Journal of Selected Topics in Signal Processing, 2015, vol. 9 (n° 6), pp. 1117-1127. ⟨10.1109/JSTSP.2015.2407855⟩. ⟨hal-01187286⟩
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