<?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>2027-8306</journal-id>
<journal-title><![CDATA[Revista de Investigación, Desarrollo e Innovación]]></journal-title>
<abbrev-journal-title><![CDATA[Revista Investig. Desarro. Innov.]]></abbrev-journal-title>
<issn>2027-8306</issn>
<publisher>
<publisher-name><![CDATA[Universidad Pedagógica y Tecnológica de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2027-83062021000200601</article-id>
<article-id pub-id-type="doi">10.19053/20278306.v11.n3.2021.13359</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Spectral denoising in hyperspectral imaging using the discrete wavelet transform]]></article-title>
<article-title xml:lang="es"><![CDATA[Reducción de ruido espectral en imágenes hiperespectrales mediante la transformada wavelet discreta]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rincón-Fonseca]]></surname>
<given-names><![CDATA[Rafael Iván]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Velásquez-Hernández]]></surname>
<given-names><![CDATA[Carlos Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Prieto-Ortiz]]></surname>
<given-names><![CDATA[Flavio Augusto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>11</volume>
<numero>3</numero>
<fpage>601</fpage>
<lpage>616</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S2027-83062021000200601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S2027-83062021000200601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S2027-83062021000200601&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The use of hyperspectral sensors has gained relevance in agriculture due to its potential in the phytosanitary management of crops. However, these sensors are sensitive to spectral noise, which makes their real application difficult. Therefore, this work focused on the analysis of the spectral noise present in a bank of 180 hyperspectral images of mango leaves acquired in the laboratory, and the implementation of a denoising technique based on the discrete wavelet transform. The noise analysis consisted in the identification of the highest noisy bands, while the performance of the technique was based on the PSNR and SNR metrics. As a result, it was determined that the spectral noise was present at the ends of the spectrum (417-421nm and 969-994nm) and that the Neigh-Shrink method achieved a SNR of the order of 1011 with respect to the order of 102 of the original spectrum.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El uso de sensores hiperespectrales ha tomado relevancia en la agricultura, debido a su potencial en el manejo fitosanitario de cultivos. Sin embargo, estos sensores son sensibles al registro de ruido espectral, lo cual dificulta su aplicación real. Por lo anterior, este trabajo se centró en el análisis del ruido espectral presente en un banco de 180 imágenes hiperespectrales de hojas de mango adquiridas en laboratorio, y la implementación de una técnica de reducción de ruido basada en la transformada discreta de wavelet. El análisis de ruido consistió en la identificación de las bandas de mayor ruido, mientras que el desempeño de la técnica fue medido con las métricas PSNR y SNR. Como resultado, se determinó que el ruido espectral estuvo presente en los extremos del espectro (417-421nm y 969-994nm), mientras que el método Neigh-Shrink alcanzó un SNR del orden de 1011 con respecto al orden de 102 del espectro original.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[HSI]]></kwd>
<kwd lng="en"><![CDATA[spectral denoising]]></kwd>
<kwd lng="en"><![CDATA[wavelet transform]]></kwd>
<kwd lng="en"><![CDATA[hyperspectral analysis]]></kwd>
<kwd lng="es"><![CDATA[HSI]]></kwd>
<kwd lng="es"><![CDATA[reducción de ruido espectral]]></kwd>
<kwd lng="es"><![CDATA[transformada wavelet]]></kwd>
<kwd lng="es"><![CDATA[análisis hiperespectral]]></kwd>
</kwd-group>
</article-meta>
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