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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprsarchives-XL-2-W3-65-2014</article-id>
<title-group>
<article-title>AN ADAPTIVE POLYGONAL CENTROIDAL VORONOI TESSELLATION ALGORITHM FOR SEGMENTATION OF NOISY SAR IMAGES</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Askari</surname>
<given-names>G.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Li</surname>
<given-names>Y.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>MoezziNasab</surname>
<given-names>R.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Earth Science, Damghan University, Damghan, 36716-41167, Iran</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University,Fuxin, Liaoning 123000, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>22</day>
<month>10</month>
<year>2014</year>
</pub-date>
<volume>XL-2/W3</volume>
<fpage>65</fpage>
<lpage>68</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2014 G. Askari et al.</copyright-statement>
<copyright-year>2014</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XL-2-W3/65/2014/isprs-archives-XL-2-W3-65-2014.html">This article is available from https://isprs-archives.copernicus.org/articles/XL-2-W3/65/2014/isprs-archives-XL-2-W3-65-2014.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XL-2-W3/65/2014/isprs-archives-XL-2-W3-65-2014.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XL-2-W3/65/2014/isprs-archives-XL-2-W3-65-2014.pdf</self-uri>
<abstract>
<p>In this research, a fast, adaptive and user friendly segmentation methodology is developed for highly speckled SAR images. The
developed region based centroidal Voronoi tessellation (R-BCVT) algorithm is a kind of polygon-based clustering approach in
which the algorithm attempts to (1) split the image domain into j numbers of centroidal Voronoi polygons (2) assign each polygon a
label randomly, then (3) classify the image into k cluster iteratively to satisfy optimum segmentation, and finally a k-mean clustering
method refine the detected boundaries of homogeneous regions. The advantages of the novel method arise from adaptively,
simplicity and rapidity as well as low sensitivity of the model to speckle noise.</p>
</abstract>
<counts><page-count count="4"/></counts>
</article-meta>
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