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ROBUST AND EFFECTIVE HYPERSPECTRAL PANSHARPENING USING SPATIO-SPECTRAL TOTAL VARIATION

Citation Author(s):
Shunsuke Ono, Itsuo Kumazawa
Submitted by:
Saori Takeyama
Last updated:
24 April 2018 - 3:30am
Document Type:
Poster
Event:
Categories:
 

Acquiring high-resolution hyperspectral (HS) images is a very challenging task. To this end, hyperspectral pansharpening techniques have been widely studied, which estimate an HS image of high spatial and spectral resolution (high HS image) from a pair of an HS image of high spectral resolution but low spatial resolution (low HS image) and a high spatial resolution panchromatic (PAN) image. However, since these methods do not fully utilize the piecewise-smoothness of spectral information on HS images in estimation, they tend to produce spectral distortion when the low HS image contains noise. To tackle this issue, we propose a new hyperspectral pansharpening method using a spatio-spectral regularization. Our method not only effectively exploits observed information but also properly promotes the spatio-spectral piecewise-smoothness of the resulting high HS image, leading to high quality and robust estimation. The proposed method is reduced to a nonsmooth convex optimization problem, which is efficiently solved by a primal-dual
splitting method. Our experiments demonstrate the advantages of our method over existing hyperspectral pansharpening methods.

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