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Fusion of hyperspectral and panchromatic images with very high spatial resolution

Abstract : Standard pansharpening aims at fusing a panchromatic image with a multispectral image in order to synthesize an image with the high spatial resolution of the former and the spectral resolution of the latter. In the last decade many pansharpening algorithms have been presented in the literature using multispectral data. With the increasing availability of hyperspectral systems, these methods are now extending to hyperspectral pansharpening, i.e. the fusion of a panchromatic image with a high spatial resolution and a hyperspectral image with a coarser spatial resolution. However, state of the art hyperspectral pansharpening methods usually do not consider the problem of the mixed pixels. Their goal is solely to preserve the spectral information while adding spatial information. In this thesis, in a first part, we present the state-of-the-art methods and analysed them to identified there performances and limitations. In a second part, we present an approach to actually deal with mixed pixels as a pre-processing step before performing the fusion. This improves the result by adding missing spectral information that is not directly available in the hyperspectral image because of the mixed pixels. The performances of our proposed approach are assessed on different real data sets, with different spectral and spatial resolutions and corresponding to different contexts. They are compared qualitatively and quantitatively with state of the art methods, both at a global and a local scale.
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Laëtitia Loncan. Fusion of hyperspectral and panchromatic images with very high spatial resolution. Signal and Image Processing. Université Grenoble Alpes, 2016. English. ⟨NNT : 2016GREAT065⟩. ⟨tel-01490225⟩

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