July 5, 2020 Figure Open Access

Rasti, Behnood; Ghamisi, Pedram; Seidel, Peter; Lorenz, Sandra; Gloaguen, Richard

DCAT Export

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        <foaf:name>Ghamisi, Pedram</foaf:name>
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        <foaf:name>Lorenz, Sandra</foaf:name>
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        <foaf:name>Gloaguen, Richard</foaf:name>
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    <dct:title>Data for: "Multi Optical Sensor Fusion for Mineral Mapping of Core Samples"</dct:title>
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    <dcat:keyword>Multi-sensor data</dcat:keyword>
    <dcat:keyword>optical sensor</dcat:keyword>
    <dcat:keyword>hyperspectral</dcat:keyword>
    <dcat:keyword>hyperspectral mixed sparse and Gaussian noise reduction (HyMiNoR)</dcat:keyword>
    <dcat:keyword>spectral imaging</dcat:keyword>
    <dcat:keyword>data fusion</dcat:keyword>
    <dcat:keyword>feature extraction</dcat:keyword>
    <dcat:keyword>dimensionality reduction</dcat:keyword>
    <dcat:keyword>support vector machine (SVM)</dcat:keyword>
    <dcat:keyword>sparse and smooth low-rank analysis (SSLRA)</dcat:keyword>
    <dcat:keyword>orthogonal total variation component analysis (OTVCA)</dcat:keyword>
    <dcat:keyword>mineral exploration</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-07-05</dct:issued>
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    <dct:description>&lt;p&gt;Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineral mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineral mapping. The concept includes i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination and iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows to fuse heterogeneous data at variable resolutions, scales, and spectral ranges as well as improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.&lt;/p&gt;</dct:description>
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