<?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>0121-7488</journal-id>
<journal-title><![CDATA[Ciencia en Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Ciencia en Desarrollo]]></abbrev-journal-title>
<issn>0121-7488</issn>
<publisher>
<publisher-name><![CDATA[Universidad Pedagógica y Tecnológica de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-74882025000200115</article-id>
<article-id pub-id-type="doi">10.19053/uptc.01217488.v16.n2.2025.18900</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Proposal of a computational method for the detection of asbestos-cement in hyperspectral images based on spectral signature binarization and Hamming distance]]></article-title>
<article-title xml:lang="es"><![CDATA[Propuesta de método computacional para la detección de asbesto-cemento en imágenes hiperespectrales a partir de la binarización de la firma espectral y la distancia de Hamming]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chanchí-Golondrino]]></surname>
<given-names><![CDATA[Gabriel Elias]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ospina-Alarcón]]></surname>
<given-names><![CDATA[Manuel Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Saba]]></surname>
<given-names><![CDATA[Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Cartagena Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>16</volume>
<numero>2</numero>
<fpage>115</fpage>
<lpage>122</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-74882025000200115&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-74882025000200115&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-74882025000200115&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract One of the challenges in the field of hyperspectral imaging is the development of computationally effective and efficient methods for material detection given the large volume of data in these images. Based on this, this article proposes a computational method for detecting asbestos-cement as a contribution, using the binarization of the spectral signature of asbestos and its comparison with the binarized spectral signatures of the image pixels through the normalized Hamming distance. As a result of this research, the method was implemented using open-source libraries (spectral, numpy, matplotlib, and pandas) and was applied as a case study to a hyperspectral image of the Manga neighborhood in the city of Cartagena. The proposed method. The effectiveness and efficiency of the proposed method were compared with the correlation method, showing that the proposed method has similar effectiveness to the correlation method and is 4.92% more efficient. Based on the obtained results, the proposed method is a competitive alternative for replication and extrapolation in the detection of different materials in hyperspectral images. Likewise, given its efficiency results, this method can be integrated into material monitoring systems based on remote sensing.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Uno de los desafíos en el campo de las imágenes hiperespectrales es contar con métodos eficaces y eficientes computacionalmente que permitan la detección de materiales ante el gran volumen de datos de estas imágenes. A partir de lo anterior, en este artículo se propone como contribución un método computacional para la detección de asbesto-cemento, a partir de la binarización de la firma espectral de asbesto y su comparación con las firmas espectrales binarizadas de los píxeles de la imagen mediante la distancia normalizada de Hamming. Como resultado de esta investigación, se implementó el método mediante el uso de las librerías de código abierto (spectral, numpy, matplotlib y pandas) y se desplegó a modo de caso de estudio sobre una imagen hiperespectral del barrio Manga de la ciudad de Cartagena. El método propuesto. La eficacia y la eficiencia del método propuesto fueron comparadas con respecto al método de correlación, obteniendo que el método propuesto tiene una eficacia similar al método de la correlación y es 4.92% más eficiente. A partir de los resultados obtenidos, el método propuesto es una alternativa competitiva para ser replicado y extrapolado en la detección de diferentes materiales en imágenes hiperespectrales. Así mismo, dado los resultados a nivel de eficiencia, este método puede ser integrado en sistemas de monitorización de materiales basados en sensado remoto.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Asbestos detection]]></kwd>
<kwd lng="en"><![CDATA[Hamming distance]]></kwd>
<kwd lng="en"><![CDATA[hyperspectral images]]></kwd>
<kwd lng="en"><![CDATA[remote sensing]]></kwd>
<kwd lng="es"><![CDATA[Detección de asbesto]]></kwd>
<kwd lng="es"><![CDATA[distancia de Hamming]]></kwd>
<kwd lng="es"><![CDATA[imágenes hiperespectrales]]></kwd>
<kwd lng="es"><![CDATA[sensado remoto]]></kwd>
</kwd-group>
</article-meta>
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