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Bayesian Fusion of Hyperspectral and Multispectral images

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If you have a question about this talk, please contact Fredrik Lindsten.

Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands, has opened a new range of relevant applications, such as target detection, classification and spectral unmixing. While HS sensors provide abundant spectral information, their spatial resolution is generally more limited. To obtain images with good spectral and spatial resolutions, the remote sensing community has been devoting increasing research efforts to the problem of fusing HS with multispectral (MS) or panchromatic (PAN) images, which owns much higher spatial resolution. Since the fusion problem is usually ill-posed, the Bayesian methodology offers a convenient way to regularize the problem by defining appropriate prior distribution for the scene of interest. In this talk, different state-of-the-art Bayesian fusion techniques for remotely sensed multi-band images will be presented.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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