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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/isprs-archives-XLI-B2-21-2016</article-id>
<title-group>
<article-title>A PROBABILITY-BASED STATISTICAL METHOD TO EXTRACT WATER BODY OF  TM IMAGES WITH MISSING INFORMATION</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Lian</surname>
<given-names>Shizhong</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>Chen</surname>
<given-names>Jiangping</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>Luo</surname>
<given-names>Minghai</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Wuhan Geomatics Institute, Wuhan, Hubei, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>07</day>
<month>06</month>
<year>2016</year>
</pub-date>
<volume>XLI-B2</volume>
<fpage>21</fpage>
<lpage>26</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2016 Shizhong Lian et al.</copyright-statement>
<copyright-year>2016</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/XLI-B2/21/2016/isprs-archives-XLI-B2-21-2016.html">This article is available from https://isprs-archives.copernicus.org/articles/XLI-B2/21/2016/isprs-archives-XLI-B2-21-2016.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLI-B2/21/2016/isprs-archives-XLI-B2-21-2016.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLI-B2/21/2016/isprs-archives-XLI-B2-21-2016.pdf</self-uri>
<abstract>
<p>Water information cannot be accurately extracted using TM images because true information is lost in some images because of
blocking clouds and missing data stripes, thereby water information cannot be accurately extracted. Water is continuously distributed
in natural conditions; thus, this paper proposed a new method of water body extraction based on probability statistics to improve the
accuracy of water information extraction of TM images with missing information. Different disturbing information of clouds and
missing data stripes are simulated. Water information is extracted using global histogram matching, local histogram matching, and
the probability-based statistical method in the simulated images. Experiments show that smaller Areal Error and higher Boundary
Recall can be obtained using this method compared with the conventional methods.</p>
</abstract>
<counts><page-count count="6"/></counts>
</article-meta>
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