<?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>0123-7799</journal-id>
<journal-title><![CDATA[TecnoLógicas]]></journal-title>
<abbrev-journal-title><![CDATA[TecnoL.]]></abbrev-journal-title>
<issn>0123-7799</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico Metropolitano - ITM]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-77992019000300006</article-id>
<article-id pub-id-type="doi">10.22430/22565337.1205</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Sparse Subspace Clustering for Hyperspectral Images using Incomplete Pixels]]></article-title>
<article-title xml:lang="es"><![CDATA[Agrupación de subespacios escasos en imágenes hiperespectrales usando pixeles incompletos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bacca]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arguello]]></surname>
<given-names><![CDATA[Henry]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Industrial de Santander  ]]></institution>
<addr-line><![CDATA[Bucaramanga ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Industrial de Santander  ]]></institution>
<addr-line><![CDATA[Bucaramanga ]]></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>22</volume>
<numero>46</numero>
<fpage>6</fpage>
<lpage>19</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-77992019000300006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-77992019000300006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-77992019000300006&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Spectral image clustering is an unsupervised method that identifies distributions of pixels using spectral information without requiring a previous training stage. Sparse subspace clustering methods assume that hyperspectral images lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering assigns spectral signatures to different subspaces, expressing each spectral signature as a sparse linear combination of all the pixels, ensuring that the non-zero elements belong to the same class. Although such methods have achieved good accuracy for unsupervised classification of hyperspectral images, their computational complexity becomes intractable as the number of pixels increases, i.e., when the spatial dimensions of the image become larger. For that reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image; subsequently, the clustering results of the missing pixels are obtained by exploiting spatial information. Specifically, this work proposes two methodologies to remove pixels: the first one is based on spatial blue noise distribution, which reduces the probability of removing neighboring pixels, and the second one is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated using three datasets, which shows that a similar accuracy is achieved when up to 50% of the pixels are removed. In addition, said framework is up to 7.9 times faster than the classification of the complete data sets.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El agrupamiento de imágenes espectrales es un método no supervisado que identifica las distribuciones de píxeles utilizando información espectral, sin necesidad de una etapa previa de entrenamiento. Los métodos basados en agrupación de subespacio escasos suponen que las imágenes hiperespectrales viven en la unión de múltiples subespacios de baja dimensión. Basado en esto, la agrupación de subespacio escasos asigna firmas espectrales a diferentes subespacios, expresando cada firma espectral como una combinación lineal escasa de todos los píxeles, garantizando que los elementos que no son cero pertenecen a la misma clase. Aunque estos métodos han demostrado una buena precisión para la clasificación no supervisada de imágenes hiperespectrales, a medida que aumenta el número de píxeles, es decir, la dimensión de la imagen es grande, la complejidad computacional se vuelve intratable. Por este motivo, este documento propone reducir el número de píxeles a clasificar en la imagen hiperespectral y, posteriormente, los resultados del agrupamiento para los píxeles faltantes se obtienen explotando la información espacial. Específicamente, este trabajo propone dos metodologías para remover los píxeles: la primera se basa en una distribución espacial de ruido azul que reduce la probabilidad de que se eliminen píxeles vecinos; la segunda, es un procedimiento de submuestreo que elimina cada dos píxeles contiguos, preservando la estructura espacial de la escena. El rendimiento del algoritmo de agrupamiento de imágenes espectrales propuesto se evalúa en tres conjuntos de datos, mostrando que se obtiene una precisión similar cuando se elimina hasta la mitad de los pixeles, además, es hasta 7.9 veces más rápido en comparación con la clasificación de los conjuntos de datos completos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Spectral images]]></kwd>
<kwd lng="en"><![CDATA[Spectral clustering]]></kwd>
<kwd lng="en"><![CDATA[Sparse subspace clustering]]></kwd>
<kwd lng="en"><![CDATA[Sub-sampling]]></kwd>
<kwd lng="en"><![CDATA[Image classification]]></kwd>
<kwd lng="es"><![CDATA[Imágenes hiperespectrales]]></kwd>
<kwd lng="es"><![CDATA[agrupación espectral]]></kwd>
<kwd lng="es"><![CDATA[agrupación de subespacios escasos]]></kwd>
<kwd lng="es"><![CDATA[submuestreo]]></kwd>
<kwd lng="es"><![CDATA[clasificación de imágenes]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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