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LiDAR-driven spatial regularization for hyperspectral unmixing

Abstract : Only a few research works consider LiDAR data while conducting hyperspectral image unmixing. However, the digital surface model derived from LiDAR can provide meaningful information, in particular when spatially regularizing the inverse problem underlain by spectral unmixing. This paper proposes a general framework for spectral unmixing that incorporates LiDAR data to inform the spatial regularization applied to the abundance maps. The proposed framework is validated and compared to existing unmixing methods that incorporate spatial information derived from the hyperspectral image itself using two different simulated data and digital surface models. Results show that the spatial regularization incorporating LiDAR data significantly improves abundance estimates.
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Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon. LiDAR-driven spatial regularization for hyperspectral unmixing. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018), Jul 2018, Valencia, Spain. pp.1740-1743. ⟨hal-02319750⟩



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