Abstract
This paper concerns the fusion of multiwave- length images obtained with different sensors observing the same scene. This is a widely addressed topic in the last decade with many potential application in remote sensing, medicine, astronomy, etc. due to the significant development of multicomponent imagery systems. A new fusion-detection algorithm of multiwavelength astronomical images based on Laplacian pyramid analysis of the input images feeding a unique vectorial hierarchical hidden Markov model is presented. This model takes into account the correlations between the observations in space, in scale and through spectral bands simultaneously in a well formulated Bayesian framework. Thus, the fusion considers the whole available information at all scales and wavelengths. The Data likelihood is formulated using copulas theory as a multidimensional Generalized Gaussian density to deal with the Non-Gaussianity of coefficients of the multiscale analysis. Results on real multiwavelength astronomical images are very satisfactory.
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