<?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>0121-3709</journal-id>
<journal-title><![CDATA[ORINOQUIA]]></journal-title>
<abbrev-journal-title><![CDATA[Orinoquia]]></abbrev-journal-title>
<issn>0121-3709</issn>
<publisher>
<publisher-name><![CDATA[Instituto de Investigaciones de la Orinoquia Colombiana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-37092017000300064</article-id>
<article-id pub-id-type="doi">10.22579/20112629.432</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificación y mapeo automático de coberturas del suelo en imágenes satelitales utilizando Redes Neuronales Convolucionales]]></article-title>
<article-title xml:lang="en"><![CDATA[Classification and automatic mapping of land covers in satellite images using Convolutional Neural Networks]]></article-title>
<article-title xml:lang="pt"><![CDATA[Classificação e mapeamento automático de coberturas do solo em imagens de satélite usando redes neurais convolucionais]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Suárez L]]></surname>
<given-names><![CDATA[Arnol S]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jiménez L]]></surname>
<given-names><![CDATA[Andrés F]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castro-Franco]]></surname>
<given-names><![CDATA[Mauricio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz-Roa]]></surname>
<given-names><![CDATA[Angel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de los Llanos Facultad de Ciencias Básicas e Ingeniería Grupo de investigación Macrypt]]></institution>
<addr-line><![CDATA[Villavicencio ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,UNAL Grupo de investigación Un-Robot estudiante de doctorado en ingeniería - mecánica mecatrónica]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,CONICET estudiante de Posdoctorado en Ciencias Agrarias Consejo Nacional de Investigaciones Científicas y Técnicas ]]></institution>
<addr-line><![CDATA[Buenos Aires ]]></addr-line>
<country>Argentina</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de los Llanos Facultad de Ciencias Básicas e Ingeniería grupo de Investigación GITECX]]></institution>
<addr-line><![CDATA[Villavicencio ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2017</year>
</pub-date>
<volume>21</volume>
<fpage>64</fpage>
<lpage>75</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-37092017000300064&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-37092017000300064&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-37092017000300064&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La clasificación de cobertura del suelo es importante para estudios de cambio climático y monitoreo de servicios ecosistémicos. Los métodos convencionales de clasificación de coberturas se realizan mediante la interpretación visual de imágenes satelitales, lo cual es costoso, dispendioso e impreciso. Implementar métodos computacionales permite generar clasificación de coberturas en imágenes satelitales de manera automática, rápida, precisa y económica. Particularmente, los métodos de aprendizaje automático son técnicas computacionales promisorias para la estimación de cambios de cobertura del suelo. En este trabajo se presenta un método de aprendizaje automático basado en redes neuronales convolucionales de arquitectura tipo ConvNet para la clasificación automática de coberturas del suelo a partir de imágenes Landsat 5 TM. La ConvNet fue entrenada a partir de las anotaciones manuales por medio de interpretación visual sobre las imágenes satelitales con las que los expertos generaron el mapa de cobertura del parque nacional el Tuparro, de los Parques Nacionales Naturales de Colombia. El modelo de validación se realizó con datos de los mapas de coberturas del Amazonas colombiano realizado por el Sistema de Información Ambiental de Colombia. Los resultados obtenidos de la diagonal de la matriz de confusión de la exactitud promedio fue de 83.27% en entrenamiento y 91.02% en validación; para la clasificación en parches entre Bosques, áreas con vegetación herbácea y/o arbustiva, áreas abiertas sin o con poca vegetación y aguas continentales.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Land cover classification is important for studies of climate change and monitoring of ecosystem services. Conventional coverage classification methods are performed by the visual interpretation of satellite imagery, which is expensive and inaccurate. Implementing computational methods could generate procedures to classify coverage in satellite images automatically, quickly, accurately and economically. Particularly, automatic learning methods are promising computational methods for estimating soil cover changes. In this work we present an automatic learning method based on convolutional neural networks of ConvNet type architecture for the automatic classification of soil coverings from Landsat 5 TM images. The ConvNet was trained from the manual annotations by means of visual interpretation on the satellite images with which the experts generated the map of Tuparro national park, of National Natural Park of Colombia. The validation model was performed with data from the Colombian Amazon cover maps made by the Colombian Environmental Information System. The results obtained from the diagonal of the confusion matrix of the average accuracy were 83.27% in training and 91.02% in validation; for the classification in patches between forests, areas with herbaceous and / or shrub vegetation, open areas with or without vegetation and Inland waters.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A classificação da cobertura da terra é importante para estudos de mudanças climáticas e monitoramento dos serviços dos ecossistemas. Os métodos convencionais de classificação de cobertura são feitos através da interpretação visual de imagens de satélite, que é caro, dispendioso e impreciso. Implementar métodos computacionais poderia gerar procedimentos de classificação de cobertura em imagenes de satélite de forma automática, rápida, precisa e econômica. Particularmente, métodos de aprendizado de máquina são promissores métodos computacionais para estimar a cobertura do solo mudanças. Neste artigo apresentamos um método de aprendizado de máquina baseado em convolutional neural tipo ConvNet rede de arquitetura para a classificação automática de cobertura do solo a partir de Landsat 5 imagens TM. O ConvNet foi treinado desde anotações manuais através da interpretação visual das imagens de satélite que os especialistas geraram o mapa de cobertura do Parque Nacional Tuparro, Colômbia Parque Nacional Natural. A validação do modelo foi realizada com cobertura de mapa de dados da Amazônia colombiana pelo Sistema de Informação Ambiental da Colômbia. Os resultados da diagonal da matriz de confusão da precisão média foi de 83,27% e Formação e 91,02% na validação; para a classificação em manchas entre florestas, áreas com vegetação herbácea e / ou arbusto, áreas abertas com poucamou nenhuma vegetação águas interiores.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[coberturas de suelo]]></kwd>
<kwd lng="es"><![CDATA[parques naturales]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales convolucionales]]></kwd>
<kwd lng="es"><![CDATA[teledetección.]]></kwd>
<kwd lng="en"><![CDATA[Automatic learning]]></kwd>
<kwd lng="en"><![CDATA[land cover]]></kwd>
<kwd lng="en"><![CDATA[natural parks]]></kwd>
<kwd lng="en"><![CDATA[convolutional neural networks]]></kwd>
<kwd lng="en"><![CDATA[remote sensing.]]></kwd>
<kwd lng="pt"><![CDATA[Aprendizagem de máquina]]></kwd>
<kwd lng="pt"><![CDATA[cobertura do solo]]></kwd>
<kwd lng="pt"><![CDATA[parques naturais]]></kwd>
<kwd lng="pt"><![CDATA[rede neural convolutional]]></kwd>
<kwd lng="pt"><![CDATA[sensoriamento remoto.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Acevedo]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<source><![CDATA[Grupo de planeacion y manejo, S. D. G. Y. M. D. A. P]]></source>
<year>2012</year>
<publisher-name><![CDATA[Parques nacionales naturales de colombia]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="">
<collab>Axesnet S.a.S.</collab>
<source><![CDATA[Sistema de Informacion Ambiental de Colombia SIAC]]></source>
<year>2012</year>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Backoulou]]></surname>
<given-names><![CDATA[GF]]></given-names>
</name>
<name>
<surname><![CDATA[Elliott]]></surname>
<given-names><![CDATA[NC]]></given-names>
</name>
<name>
<surname><![CDATA[Giles]]></surname>
<given-names><![CDATA[KL]]></given-names>
</name>
<name>
<surname><![CDATA[Mirik]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes]]></article-title>
<source><![CDATA[Computers and Electronics in Agriculture]]></source>
<year>2015</year>
<numero>115</numero>
<issue>115</issue>
<page-range>34-9</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bokusheva]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Kogan]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Vitkovskaya]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Conradt]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Batyrbayeva]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Satellite-based vegetation health indices as a criteria for insuring against drought-related yield losses]]></article-title>
<source><![CDATA[Agricultural and Forest Meteorology]]></source>
<year>2016</year>
<numero>220</numero>
<issue>220</issue>
<page-range>200-6</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cruz-Roa]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Arévalo]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Judkins]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Madabhushi]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<source><![CDATA[A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning]]></source>
<year>2015</year>
<conf-name><![CDATA[ International Symposium on Medical Information Processing and Analysis, 9681, 968103]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cruz-Roa]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Basavanhally]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Gilmore]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Feldman]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Ganesan]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Madabhushi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks]]></article-title>
<source><![CDATA[Proc. SPIE]]></source>
<year>2014</year>
<volume>9041</volume>
<numero>216</numero>
<issue>216</issue>
<page-range>904103-15</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Eisavi]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
<name>
<surname><![CDATA[Homayouni]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Yazdi]]></surname>
<given-names><![CDATA[AM]]></given-names>
</name>
<name>
<surname><![CDATA[Alimohammadi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Land cover mapping based on random forest classification of multitemporal spectral and thermal images]]></article-title>
<source><![CDATA[Environmental Monitoring and Assessment]]></source>
<year>2015</year>
<volume>187</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>1-14</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[JT]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Gong]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[An analysis of convolutional neural networks for speech recognition]]></source>
<year>2015</year>
<conf-name><![CDATA[ In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>4989-93</page-range><publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="book">
<collab>IDEAM, IGAC, &amp; CORMAGDALENA.</collab>
<source><![CDATA[Mapa de Cobertura de la Tierra Cuenca Magdalena-Cauca: Metodología CORINE Land Cover adaptada para Colombia a escala 1:100.000]]></source>
<year>2008</year>
<volume>1</volume>
<publisher-name><![CDATA[Instituto de Hidrología, Meteorología y Estudios Ambientales, Instituto Geográfico Agustín Codazzi y Corporación Autónoma Regional del río Grande de la Magdalena]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Krizhevsky]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Sutskever]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Hinton]]></surname>
<given-names><![CDATA[GE.]]></given-names>
</name>
</person-group>
<source><![CDATA[Image Net Classification with Deep Convolutional Neural Networks. Advances In Neural Information Processing Systems]]></source>
<year>2012</year>
<page-range>1-9</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[LM]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A self-trained semisupervised SVM approach to the remote sensing land cover classification]]></article-title>
<source><![CDATA[Computers and Geosciences]]></source>
<year>2013</year>
<numero>59</numero>
<issue>59</issue>
<page-range>98-107</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Martin]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Newman]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Aber]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Congalton]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Determining forest species composition using high spectral resolution remote sensing data]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>1998</year>
<volume>65</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>249-54</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="">
<collab>Ministerio del Medio Ambiente.</collab>
<source><![CDATA[Leyenda nacional de coberturas de la tierra]]></source>
<year>2010</year>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Perlin]]></surname>
<given-names><![CDATA[HA]]></given-names>
</name>
<name>
<surname><![CDATA[Lopes]]></surname>
<given-names><![CDATA[HS.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Extracting human attributes using a convolutional neural network approach]]></article-title>
<source><![CDATA[Pattern Recognition Letters]]></source>
<year>2015</year>
<numero>68</numero>
<issue>68</issue>
<page-range>250-9</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rodriguez-Galiano]]></surname>
<given-names><![CDATA[VF]]></given-names>
</name>
<name>
<surname><![CDATA[Ghimire]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Rogan]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Chica-Olmo]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[RigolSanchez]]></surname>
<given-names><![CDATA[JP.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An assessment of the effectiveness of a random forest classifier for land-cover classification]]></article-title>
<source><![CDATA[ISPRS Journal ofPhotogrammetry and Remote Sensing]]></source>
<year>2012</year>
<numero>67</numero>
<issue>67</issue>
<page-range>93-104</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rujoiu-Mare]]></surname>
<given-names><![CDATA[MR]]></given-names>
</name>
<name>
<surname><![CDATA[Mihai]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Mapping Land Cover Using Remote Sensing Data and GIS Techniques: A Case Study of Prahova Subcarpathians]]></article-title>
<source><![CDATA[Procedia Environmental Sciences]]></source>
<year>2016</year>
<numero>32</numero>
<issue>32</issue>
<page-range>244-55</page-range></nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Thonfeld]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Feilhauer]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Braun]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Menz]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data]]></article-title>
<source><![CDATA[International Journal of Applied Earth Observation and Geoinformation]]></source>
<year>2016</year>
<numero>50</numero>
<issue>50</issue>
<page-range>131-40</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Cruz-Roa]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Basavanhally]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Gilmore]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Shih]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Feldman]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Madabhushi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features]]></article-title>
<source><![CDATA[Journal of Medical Imaging (Bellingham, Wash.).]]></source>
<year>2014</year>
<volume>1</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>34003</page-range></nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Warner]]></surname>
<given-names><![CDATA[TA]]></given-names>
</name>
<name>
<surname><![CDATA[Foody]]></surname>
<given-names><![CDATA[GM]]></given-names>
</name>
<name>
<surname><![CDATA[Nellis]]></surname>
<given-names><![CDATA[MD.]]></given-names>
</name>
</person-group>
<source><![CDATA[The SAGE Handbook of Remote Sensing]]></source>
<year>2009</year>
<numero>504</numero>
<issue>504</issue>
</nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Study of land cover classification based on knowledge rules using high-resolution remote sensing images]]></article-title>
<source><![CDATA[Expert Systems with Applications]]></source>
<year>2011</year>
<volume>38</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>3647-52</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
