Item talk:Q265304

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   "name": "Introduction to the Python Hyperspectral Analysis Tool (PyHAT)",
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       "value": 70249418
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       "value": "10.1016/B978-0-12-818721-0.00012-4",
       "url": "https://doi.org/10.1016/B978-0-12-818721-0.00012-4"
     }
   ],
   "inLanguage": "en",
   "datePublished": "2022",
   "dateModified": "2023-10-10",
   "abstract": "Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and\u00a0hyperspectral data\u00a0sets and apply\u00a0spectroscopic analysis\u00a0techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to support exploratory spectral data analysis, the derivation of mission generated thematic products, and the application of statistical learning methods to spectral data. We present a general overview of the software architecture and identify the classes of users we seek to support. Four case studies demonstrate the use for both orbital and in-situ (landed) hyperspectral data.",
   "description": "36 p.",
   "publisher": {
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     "name": "Elsevier"
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   "author": [
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       "name": "Laura, Jason",
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       "name": "Gaddis, Lisa R.",
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     },
     {
       "@type": "Person",
       "name": "Anderson, Ryan rbanderson@usgs.gov",
       "givenName": "Ryan",
       "familyName": "Anderson",
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       "name": "Aneece, Itiya",
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