<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Lisa R. Gaddis</dc:contributor>
  <dc:contributor>Ryan B. Anderson</dc:contributor>
  <dc:contributor>Itiya P. Aneece</dc:contributor>
  <dc:creator>Jason Laura</dc:creator>
  <dc:date>2022</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;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&amp;nbsp;hyperspectral data&amp;nbsp;sets and apply&amp;nbsp;&lt;/span&gt;spectroscopic analysis&lt;span&gt;&amp;nbsp;techniques 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.&lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1016/B978-0-12-818721-0.00012-4</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Elsevier</dc:publisher>
  <dc:title>Introduction to the Python Hyperspectral Analysis Tool (PyHAT)</dc:title>
  <dc:type>chapter</dc:type>
</oai_dc:dc>