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dc.contributor.authorLabitzke, Björnen_US
dc.contributor.authorUrrigshardt, Franken_US
dc.contributor.authorKolb, Andreasen_US
dc.contributor.editorMichael Bronstein and Jean Favre and Kai Hormannen_US
dc.description.abstractA major issue in multispectral data analysis stems from the concept of spectral mixture analysis, i.e. the fact that a pixel does not cover only one material but corresponds to a mixture of materials. Even though many automatic methods for spectral unmixing exist, in many practical applications, domain experts have to verify the result and sometimes have to manually adjust the set of determined materials to achieve proper spectral reconstructions. In this paper, we propose an approach to enhance the very tedious and time-consuming task of manual verification of the unmixing and optional refinement of the materials. Our visual analysis approach comprises different techniques for an expressive spectral error visualization, efficiently guiding the user towards spectra in the dataset which are potentially missing materials. Here, combined views allow comprehensive, local and global error inspections in parallel. We present results of our proposed approach for two domains.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.m [Computer Graphics]en_US
dc.subjectI.4.m [Image Processing and Computer Vision]en_US
dc.titleExpressive Spectral Error Visualization for Enhanced Spectral Unmixingen_US
dc.description.seriesinformationVision, Modeling & Visualizationen_US

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  • VMV13
    ISBN 978-3-905674-51-4

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