<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462020000200497</article-id>
<article-id pub-id-type="doi">10.13053/cys-24-2-3374</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Local Binary Ensemble based Self-training for Semi-supervised Classification of Hyperspectral Remote Sensing Images]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[Pangambam Sendash]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[Vijendra Pratap]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pandey]]></surname>
<given-names><![CDATA[Manish Kumar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Karthikeyan]]></surname>
<given-names><![CDATA[Subbiah]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Banaras Hindu University Insitute of Science Department of Computer Science]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>India</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<fpage>497</fpage>
<lpage>509</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462020000200497&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462020000200497&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462020000200497&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Supervised classification of hyperspectral remote sensing images is still challenging due to the scarcity of enough labelled samples. Semi-supervised methods have been adopted to handle this issue. Self-training is a popular semi-supervised technique which is widely used for training a classifier with limited labelled data and a large quantity of unlabeled data. However, traditional self-training approaches often give poor classification results in high dimensional data. In the current work, a novel efficient self-training approach for handling the deficiency of labelled samples for semi-supervised classification of hyperspectral remote sensing images is proposed. The proposed method first trains an ensemble of locally specialized supervised binary classifiers independently by using the dimensionally reduced spectral feature vectors of few available labelled samples. The trained local binary classifiers are then used to extend the labelled set by iterative addition of highly informative unlabeled samples to it by exploiting both the spectral and spatial information of the hyperspectral image. The classifiers are then retrained with the extended dataset in a batchwise manner and the procedure is repeated until adequate quantity of labelled samples are generated. Finally, a supervised multiclass classifier is trained on the extended dataset to produce the final classification map. Experimental results on two benchmark hyperspectral image datasets prove the effectiveness of the proposed method over supervised and traditional self-training based semi-supervised pixelwise classification approach in terms of different classification measures.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Remote sensing]]></kwd>
<kwd lng="en"><![CDATA[hyperspectral image analysis]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[semi-supervised learning]]></kwd>
<kwd lng="en"><![CDATA[self-training]]></kwd>
<kwd lng="en"><![CDATA[ensembles]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>1</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Adam]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Mutanga]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Rugege]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review]]></article-title>
<source><![CDATA[Wetlands Ecology and Management]]></source>
<year>2010</year>
<volume>18</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>281-96</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>2</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Allwein]]></surname>
<given-names><![CDATA[E. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Schapire]]></surname>
<given-names><![CDATA[R. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Singer]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Reducing multiclass to binary: A unifying approach for margin classifiers]]></article-title>
<source><![CDATA[Journal of Machine Learning Research]]></source>
<year>2001</year>
<volume>1</volume>
<page-range>113-41</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>3</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Aydav]]></surname>
<given-names><![CDATA[P. S. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Minz]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Granulation-based self-training for the semi-supervised classification of remote-sensing images]]></article-title>
<source><![CDATA[Granular Computing]]></source>
<year>2020</year>
<volume>5</volume>
<page-range>309-27</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>4</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bazi]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Melgani]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Toward an optimal svm classification system for hyperspectral remote sensing images]]></article-title>
<source><![CDATA[IEEE Transactions on Geoscience and Remote Sensing]]></source>
<year>2006</year>
<volume>44</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>3374-85</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>5</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bei Fang]]></surname>
<given-names><![CDATA[Ying Li]]></given-names>
</name>
<name>
<surname><![CDATA[Haokui Zhang]]></surname>
<given-names><![CDATA[J. C.-W. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semi-supervised deep learning classification for hyperspectral image based on dual-strategy sample selection]]></article-title>
<source><![CDATA[Remote Sensing]]></source>
<year>2018</year>
<volume>10</volume>
<numero>4</numero>
<issue>4</issue>
</nlm-citation>
</ref>
<ref id="B6">
<label>6</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Camps-Valls]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Bandos Marsheva]]></surname>
<given-names><![CDATA[T. V.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semi-supervised graph-based hyperspectral image classification]]></article-title>
<source><![CDATA[IEEE Transactions on Geoscience and Remote Sensing]]></source>
<year>2007</year>
<volume>45</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>3044-54</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>7</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cao]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Tian]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhuo]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<source><![CDATA[Salient target detection in hyperspectral images using spectral saliency]]></source>
<year>2015</year>
<conf-name><![CDATA[ IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1086-90</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>8</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chapelle]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Schlkopf]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Zien]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Semi-Supervised Learning]]></source>
<year>2010</year>
<edition>1</edition>
<publisher-name><![CDATA[The MIT Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B9">
<label>9</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chapelle]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Sindhwani]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Keerthi]]></surname>
<given-names><![CDATA[S. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Optimization techniques for semi-supervised support vector machines]]></article-title>
<source><![CDATA[Journal of Machine Learning Research]]></source>
<year>2008</year>
<volume>9</volume>
<page-range>203-33</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>10</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chi]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Bruzzone]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semisupervised classification of hyperspectral images by SVMs optimized in the primal]]></article-title>
<source><![CDATA[IEEE Transactions on Geoscience and Remote Sensing]]></source>
<year>2007</year>
<volume>45</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1870-80</page-range></nlm-citation>
</ref>
<ref id="B11">
<label>11</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chiu]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fuzzy model identification based on cluster estimation]]></article-title>
<source><![CDATA[Journal of Intelligent and Fuzzy Systems]]></source>
<year>1994</year>
<volume>2</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>267-78</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>12</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cortes]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Vapnik]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Support vector networks]]></article-title>
<source><![CDATA[Machine Learning]]></source>
<year>1995</year>
<volume>20</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>273-97</page-range></nlm-citation>
</ref>
<ref id="B13">
<label>13</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cui]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Xie]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Hao]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Cui]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Lu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semi-supervised classification of hyperspectral images based on extended label propagation and rolling guidance filtering]]></article-title>
<source><![CDATA[Remote Sensing]]></source>
<year>2018</year>
<volume>10</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>515</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>14</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dai]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semisupervised scene classification for remote sensing images: A method based on convolutional neural networks and ensemble learning]]></article-title>
<source><![CDATA[IEEE Geoscience and Remote Sensing Letters]]></source>
<year>2019</year>
<volume>PP</volume>
<page-range>1-5</page-range></nlm-citation>
</ref>
<ref id="B15">
<label>15</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Demir]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Erturk]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Hyperspectral image classification using relevance vector machines]]></article-title>
<source><![CDATA[IEEE Geoscience and Remote Sensing Letters]]></source>
<year>2007</year>
<volume>4</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>586-90</page-range></nlm-citation>
</ref>
<ref id="B16">
<label>16</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Foody]]></surname>
<given-names><![CDATA[G. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Mathur]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A relative evaluation of multiclass image classification by support vector machines]]></article-title>
<source><![CDATA[IEEE Transactions on Geoscience and Remote Sensing]]></source>
<year>2004</year>
<volume>42</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1335-43</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>17</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Govender]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Chetty]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Bulcock]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A review of hyperspectral remote sensing and its application in vegetation and water resource studies]]></article-title>
<source><![CDATA[Water SA]]></source>
<year>2009</year>
<volume>33</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>145-51</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>18</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Generative adversarial networks-based semi-supervised learning for hyperspectral image classification]]></article-title>
<source><![CDATA[Remote Sensing]]></source>
<year>2017</year>
<volume>9</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>1042</page-range></nlm-citation>
</ref>
<ref id="B19">
<label>19</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Feng]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Hyperspectral remote sensing image change detection based on tensor and deep learning]]></article-title>
<source><![CDATA[Journal of Visual Communication and Image Representation]]></source>
<year>2018</year>
<volume>58</volume>
<page-range>233-44</page-range></nlm-citation>
</ref>
<ref id="B20">
<label>20</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jain]]></surname>
<given-names><![CDATA[A. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Murty]]></surname>
<given-names><![CDATA[M. N.]]></given-names>
</name>
<name>
<surname><![CDATA[Flynn]]></surname>
<given-names><![CDATA[P. J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Data clustering: A review]]></article-title>
<source><![CDATA[ACM Comput. Surv.]]></source>
<year>1999</year>
<volume>31</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>399-404</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>21</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jamali]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Jönsson]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Eklundh]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Ardö]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Seaquist]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Detecting changes in vegetation trends using time series segmentation]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>2015</year>
<volume>156</volume>
<page-range>182-95</page-range></nlm-citation>
</ref>
<ref id="B22">
<label>22</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kang]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhuo]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Duan]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semi-supervised deep learning for hyperspectral image classification]]></article-title>
<source><![CDATA[Remote Sensing Letters]]></source>
<year>2019</year>
<volume>10</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>353-62</page-range></nlm-citation>
</ref>
<ref id="B23">
<label>23</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Khan]]></surname>
<given-names><![CDATA[S. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmad]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Cluster center initialization algorithm for K-means clustering]]></article-title>
<source><![CDATA[Pattern Recognition Letters]]></source>
<year>2004</year>
<volume>25</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>1293-302</page-range></nlm-citation>
</ref>
<ref id="B24">
<label>24</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Krawczyk]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Wo&#378;niak]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Herrera]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[On the usefulness of one-class classifier ensembles for decomposition of multi-class problems]]></article-title>
<source><![CDATA[Pattern Recognition]]></source>
<year>2015</year>
<volume>48</volume>
<numero>12</numero>
<issue>12</issue>
<page-range>3969-82</page-range></nlm-citation>
</ref>
<ref id="B25">
<label>25</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lanthier]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Bannari]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Haboudane]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Miller]]></surname>
<given-names><![CDATA[J. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Tremblay]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<source><![CDATA[Hyperspectral data segmentation and classification in precision agriculture: A multi-scale analysis]]></source>
<year>2008</year>
<volume>2</volume>
<conf-name><![CDATA[ IEEE International Geoscience and Remote Sensing Symposium (IGARSS)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>II-585&#8211;II&#8211;588</page-range><publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B26">
<label>26</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Tan]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Xue]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A semi-supervised convolutional neural network for hyperspectral image classification]]></article-title>
<source><![CDATA[Remote Sensing Letters]]></source>
<year>2017</year>
<volume>8</volume>
<numero>9</numero>
<issue>9</issue>
<page-range>839-48</page-range></nlm-citation>
</ref>
<ref id="B27">
<label>27</label><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[L.-m.]]></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 &amp; Geosciences]]></source>
<year>2013</year>
<volume>59</volume>
<page-range>98-107</page-range></nlm-citation>
</ref>
<ref id="B28">
<label>28</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Hyperspectral image classification based on semi-supervised rotation forest]]></article-title>
<source><![CDATA[Remote Sensing]]></source>
<year>2017</year>
<volume>9</volume>
<numero>9</numero>
<issue>9</issue>
</nlm-citation>
</ref>
<ref id="B29">
<label>29</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Crawford]]></surname>
<given-names><![CDATA[M. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Tian]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Local manifold learning-based k -nearest-neighbor for hyperspectral image classification]]></article-title>
<source><![CDATA[IEEE Transactions on Geoscience and Remote Sensing]]></source>
<year>2010</year>
<volume>48</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>4099-109</page-range></nlm-citation>
</ref>
<ref id="B30">
<label>30</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ju]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification]]></article-title>
<source><![CDATA[Pattern Recognition Letters]]></source>
<year>2016</year>
<volume>83</volume>
<page-range>133-42</page-range></nlm-citation>
</ref>
<ref id="B31">
<label>31</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning]]></article-title>
<source><![CDATA[ISPRS Journal of Photogrammetry and Remote Sensing]]></source>
<year>2016</year>
<volume>120</volume>
<page-range>99-107</page-range></nlm-citation>
</ref>
<ref id="B32">
<label>32</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Maulik]]></surname>
<given-names><![CDATA[U.]]></given-names>
</name>
<name>
<surname><![CDATA[Chakraborty]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A self-trained ensemble with semisupervised SVM: An application to pixel classification of remote sensing imagery]]></article-title>
<source><![CDATA[Pattern Recognition]]></source>
<year>2011</year>
<volume>44</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>615-23</page-range></nlm-citation>
</ref>
<ref id="B33">
<label>33</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mohamed]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Farag]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Advanced algorithms for bayesian classification in high dimensional spaces with applications in hyperspectral image segmentation]]></source>
<year>2005</year>
<volume>2</volume>
<conf-name><![CDATA[ IEEE International Conference on Image Processing 2005]]></conf-name>
<conf-loc> </conf-loc>
<page-range>II-646</page-range><publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B34">
<label>34</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pan]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[MugNet: Deep learning for hyperspectral image classification using limited samples]]></article-title>
<source><![CDATA[ISPRS Journal of Photogrammetry and Remote Sensing]]></source>
<year>2018</year>
<volume>145</volume>
<page-range>108-19</page-range></nlm-citation>
</ref>
<ref id="B35">
<label>35</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Qin]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Shang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Tian]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Tang]]></surname>
<given-names><![CDATA[Y. Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Spectral&#8211;spatial graph convolutional networks for semisupervised hyperspectral image classification]]></article-title>
<source><![CDATA[IEEE Geoscience and Remote Sensing Letters]]></source>
<year>2019</year>
<volume>16</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>241-5</page-range></nlm-citation>
</ref>
<ref id="B36">
<label>36</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Richards]]></surname>
<given-names><![CDATA[J. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Remote Sensing Digital Image Analysis]]></source>
<year>2013</year>
<volume>5</volume>
<publisher-loc><![CDATA[Berlin, Heidelberg ]]></publisher-loc>
<publisher-name><![CDATA[Springer Berlin Heidelberg]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B37">
<label>37</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Roli]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Serpico]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Bruzzone]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<source><![CDATA[Classification of multisensor remote-sensing images by multiple structured neural networks]]></source>
<year>1996</year>
<volume>4</volume>
<conf-name><![CDATA[ 13th International Conference on Pattern Recognition]]></conf-name>
<conf-loc> </conf-loc>
<page-range>180-4</page-range></nlm-citation>
</ref>
<ref id="B38">
<label>38</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Romaszewski]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[G&#322;omb]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Cholewa]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach]]></article-title>
<source><![CDATA[ISPRS Journal of Photogrammetry and Remote Sensing]]></source>
<year>2016</year>
<volume>121</volume>
<page-range>60-76</page-range></nlm-citation>
</ref>
<ref id="B39">
<label>39</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Roy]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Melgani]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Ghosh]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Blanzieri]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Ghosh]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Land-cover classification of remotely sensed images using compressive sensing having severe scarcity of labeled patterns]]></article-title>
<source><![CDATA[IEEE Geoscience and Remote Sensing Letters]]></source>
<year>2015</year>
<volume>12</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1257-61</page-range></nlm-citation>
</ref>
<ref id="B40">
<label>40</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Samat]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Miao]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Luo]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Improved hyperspectral image classification by active learning using pre-designed mixed pixels]]></article-title>
<source><![CDATA[Pattern Recognition]]></source>
<year>2016</year>
<volume>51</volume>
<page-range>43-58</page-range></nlm-citation>
</ref>
<ref id="B41">
<label>41</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shao]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Lunetta]]></surname>
<given-names><![CDATA[R. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points]]></article-title>
<source><![CDATA[ISPRS Journal of Photogrammetry and Remote Sensing]]></source>
<year>2012</year>
<volume>70</volume>
<page-range>78-87</page-range></nlm-citation>
</ref>
<ref id="B42">
<label>42</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tan]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination]]></article-title>
<source><![CDATA[ISPRS Journal of Photogrammetry and Remote Sensing]]></source>
<year>2015</year>
<volume>105</volume>
<page-range>19-29</page-range></nlm-citation>
</ref>
<ref id="B43">
<label>43</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tan]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An efficient semi-supervised classification approach for hyperspectral imagery]]></article-title>
<source><![CDATA[ISPRS Journal of Photogrammetry and Remote Sensing]]></source>
<year>2014</year>
<volume>97</volume>
<page-range>36-45</page-range></nlm-citation>
</ref>
<ref id="B44">
<label>44</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Thyagharajan]]></surname>
<given-names><![CDATA[K. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Vignesh]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Soft computing techniques for land use and land cover monitoring with multispectral remote sensing images: A review]]></article-title>
<source><![CDATA[Archives of Computational Methods in Engineering]]></source>
<year>2019</year>
<volume>26</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>275-301</page-range></nlm-citation>
</ref>
<ref id="B45">
<label>45</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semi-supervised classification framework of hyperspectral images based on the fusion evidence entropy]]></article-title>
<source><![CDATA[Multimedia Tools and Applications]]></source>
<year>2018</year>
<volume>77</volume>
<numero>9</numero>
<issue>9</issue>
<page-range>10615-33</page-range></nlm-citation>
</ref>
<ref id="B46">
<label>46</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Jia]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A novel semisupervised active-learning algorithm for hyperspectral image classification]]></article-title>
<source><![CDATA[IEEE Transactions on Geoscience and Remote Sensing]]></source>
<year>2017</year>
<volume>55</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>3071-83</page-range></nlm-citation>
</ref>
<ref id="B47">
<label>47</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wo&#378;niak]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Graña]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Corchado]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A survey of multiple classifier systems as hybrid systems]]></article-title>
<source><![CDATA[Information Fusion]]></source>
<year>2014</year>
<volume>16</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>3-17</page-range></nlm-citation>
</ref>
<ref id="B48">
<label>48</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Prasad]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Semi-supervised deep learning using pseudo labels for hyperspectral image classification]]></article-title>
<source><![CDATA[IEEE Transactions on Image Processing]]></source>
<year>2018</year>
<volume>27</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>1259-70</page-range></nlm-citation>
</ref>
<ref id="B49">
<label>49</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Younan]]></surname>
<given-names><![CDATA[N. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Particle swarm optimization-based band selection for hyperspectral target detection]]></article-title>
<source><![CDATA[IEEE Geoscience and Remote Sensing Letters]]></source>
<year>2017</year>
<volume>14</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>554-8</page-range></nlm-citation>
</ref>
<ref id="B50">
<label>50</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Hou]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Jia]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Mei]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Pseudolabel guided kernel learning for hyperspectral image classification]]></article-title>
<source><![CDATA[IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing]]></source>
<year>2019</year>
<volume>12</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>1000-11</page-range></nlm-citation>
</ref>
<ref id="B51">
<label>51</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ye]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Song]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Benediktsson]]></surname>
<given-names><![CDATA[J. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A novel semi-supervised learning framework for hyperspectral image classification]]></article-title>
<source><![CDATA[International Journal of Wavelets, Multiresolution and Information Processing]]></source>
<year>2016</year>
<volume>14</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>1640005</page-range></nlm-citation>
</ref>
<ref id="B52">
<label>52</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Hyperspectral images classification with convolutional neural network and textural feature using limited training samples]]></article-title>
<source><![CDATA[Remote Sensing Letters]]></source>
<year>2019</year>
<volume>10</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>449-58</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
