<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1909-8367</journal-id>
<journal-title><![CDATA[Entre Ciencia e Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[Entre Ciencia e Ingenieria]]></abbrev-journal-title>
<issn>1909-8367</issn>
<publisher>
<publisher-name><![CDATA[Universidad Católica de Pereira]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1909-83672019000200051</article-id>
<article-id pub-id-type="doi">10.31908/19098367.1161</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A Tool for Analysis of Spectral Indices for Remote Sensing of Vegetation and Crops Using Hyperspectral Images]]></article-title>
<article-title xml:lang="es"><![CDATA[Una herramienta para el análisis de índices espectrales para la detección remota de vegetación y cultivos utilizando imágenes hiperespectrales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruiz]]></surname>
<given-names><![CDATA[D. A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bacca]]></surname>
<given-names><![CDATA[E. B.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Caicedo]]></surname>
<given-names><![CDATA[E. F.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>13</volume>
<numero>26</numero>
<fpage>51</fpage>
<lpage>58</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1909-83672019000200051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1909-83672019000200051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1909-83672019000200051&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Food requirements in the world have increased, evidencing the necessity to improve standard techniques of agricultural production. To do so, one option is through technological elements like hyperspectral remote sensing of vegetation and crops. Remote sensing and hyperspectral imagery are not invasive methods. They allow covering large land space in a reduced amount of time. These features have done the hyper-spectral remote sensing a powerful tool used in precision agriculture. This paper presents a software application to process hyperspectral images and generating pseudo-color images computed using spectral indices. This work uses the hyperspectral images were taken by Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor, which was designed by the NASA. The software application aims to show different elements associated with the hyperspectral remote sensing of vegetation and crops. Functional tests are presented to verify the software requirements. Finally, quantitative results are reported comparing the results of the software proposes in this work with the ERDAS Imagine software tool.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Los requerimientos alimentarios en el mundo han aumentado, evidenciando la necesidad de mejorar las técnicas estándar de producción agrícola. Para abordar este problema, una alternativa de solución es la inclusión de elementos tecnológicos como el sensado remoto de vegetación y los cultivos a partir de imágenes hiperespectrales. El sensado remoto y las imágenes hiperespectrales son métodos no invasivos, que permiten monitorear grandes espacios de terreno en cantidades de tiempo reducidas. Estas características han hecho que el sensado remoto a partir de imágenes hiperespectrales sea una herramienta poderosa para desarrollo de procesos de agricultura de precisión. En este artículo se presenta una aplicación de software que permite generar y procesar índices espectrales de vegetación y sus respectivas imágenes de pseudo color, utilizando imágenes hiperespectrales. Las imágenes hiperespectrales utilizadas fueron tomadas de la base de datos del sensor Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), diseñado por la NASA. El objetivo de la aplicación de software es mostrar diferentes elementos asociados con el monitoreo remoto de vegetación y cultivos a partir de imágenes hiperespectrales. Finalmente, se presentan pruebas funcionales para verificar el cumplimiento de los requisitos del software.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Hyperspectral Images]]></kwd>
<kwd lng="en"><![CDATA[Remote Sensing]]></kwd>
<kwd lng="en"><![CDATA[Spectral Bands]]></kwd>
<kwd lng="en"><![CDATA[Spectral Indices]]></kwd>
<kwd lng="en"><![CDATA[Wavelength]]></kwd>
<kwd lng="es"><![CDATA[Imágenes hiperespectrales]]></kwd>
<kwd lng="es"><![CDATA[sensado remoto]]></kwd>
<kwd lng="es"><![CDATA[bandas espectrales]]></kwd>
<kwd lng="es"><![CDATA[índice de vegetación]]></kwd>
<kwd lng="es"><![CDATA[longitud de onda]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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