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Bayesian Fusion of Multi-Band Images

Qi Wei 1 Nicolas Dobigeon 1, 2 Jean-Yves Tourneret 3, 2
1 IRIT-SC - Signal et Communications
IRIT - Institut de recherche en informatique de Toulouse
3 IRIT-MINDS - CoMputational imagINg anD viSion
IRIT - Institut de recherche en informatique de Toulouse
Abstract : 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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Submitted on : Wednesday, August 26, 2015 - 2:59:22 PM
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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, IEEE, 2015, vol. 9 (n° 6), pp. 1117-1127. ⟨10.1109/JSTSP.2015.2407855⟩. ⟨hal-01187286⟩

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