文章基本信息

标题：Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland
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作者：Sandra Lorenz ; Sam T. Thiele ; Moritz Kirsch 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2022
卷号：7
期号：8
页码：1-15
DOI：10.3390/data7080104
语种：English
出版社：MDPI Publishing
摘要：Hyperspectral imaging is an innovative technology for non-invasive mapping, with increas- ing applications in many sectors. As with any novel technology, robust processing workﬂows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed geology from the Black Angel Mountain in Maarmorilik, West Green- land, alongside a detailed and interactive tutorial documenting relevant processing workﬂows. This contribution relies on very recent progress made on the correction, interpretation and integration of hyperspectral data in earth sciences. The possibility to fuse hyperspectral scans with 3D point cloud representations (hyperclouds) has opened up new possibilities for the mapping of complex natural targets. Spectroscopic and machine learning tools allow or the rapid and accurate characterization of geological structures in a 3D environment. Potential users can use this exemplary dataset and the associated tools to train themselves or test new algorithms. As the data and the tools have a wide range of application, we expect this contribution to beneﬁt the scientiﬁc community at large.Dataset: http://doi.org/10.14278/rodare.1642 (direct link: https://rodare.hzdr.de/record/1642).Dataset License: The dataset is published in RODARE under Creative Commons Attribution 4.0 International license. The used tools are based on the hylite toolbox that is published in github with an MIT license.
关键词：open-source dataset;hyperspectral data;spectral imaging;3D hyperclouds;photogrammetry;mineral mapping;Black Angel Maarmorilik;minimum wavelength mapping;interactive workﬂow