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- W3130484998 abstract "Sub-pixel mapping (SPM) denotes a category of image processing techniques that further enhance the results provided by spectral unmixing algorithms. While the latter is only capable to determine the fractional abundances of classes with an associated spectral signature within a certain area denoted as mixed pixel, SPM can in addition spatially locate each class separately within the mixed pixel itself, enhancing the spatial resolution of its products. Given the demands by both technological and scientific application, various approaches have been proposed; this work will focus on the ones belonging to the domain of variational frameworks, which allow to solve the intrinsic ill-posedness of inverse problems by imposing a regularization based on gradients of the desired output. As the problem of SPM may be also seen as generating a rule to associate each mixed pixel to a specific patch of mosaicked classes, we propose to create an overcomplete dictionary listing them. We present here some first investigation in this direction, by incorporating such dictionary, generated via K-SVD dictionary learning algorithm, in the formulation of an inverse problem. The algorithm is matched with a Isotropic Total Variation (ITV) regularization to provide joint spatial and spectral consistency to the results. Our tests prove that our proposed method provides better SPM product quality compared to its use in insulation and to other state of the art variational framework-based algorithms." @default.
- W3130484998 created "2021-03-01" @default.
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- W3130484998 date "2020-09-26" @default.
- W3130484998 modified "2023-10-17" @default.
- W3130484998 title "Sub-Pixel Mapping Method Based on K-SVD Dictionary Learning and Total Variation Minimization" @default.
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- W3130484998 doi "https://doi.org/10.1109/igarss39084.2020.9324142" @default.
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