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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-7-93-2014</article-id>
<title-group>
<article-title>Automatic surface classification for retrieving areas which are highly endangered by extreme rain</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Fischer</surname>
<given-names>P.</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>Krauß</surname>
<given-names>T.</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>Peters</surname>
<given-names>T.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Remote Sensing Technology Institute, German Aerospace Center (DLR), Münchener Str. 20, 82234 Wessling, Germany</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Westfälische Provinzial Versicherung AG, Provinzial-Allee 1, 48181 Münster, Germany</addr-line>
</aff>
<pub-date pub-type="epub">
<day>19</day>
<month>09</month>
<year>2014</year>
</pub-date>
<volume>XL-7</volume>
<fpage>93</fpage>
<lpage>100</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2014 P. Fischer 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>
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<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XL-7/93/2014/isprs-archives-XL-7-93-2014.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XL-7/93/2014/isprs-archives-XL-7-93-2014.pdf</self-uri>
<abstract>
<p>In this case study, an approach for finding regions endangered by extreme rain is presented. The approach is based on the assumption
that sinks in the surface are more endangered than their surroundings. The surface data, which are the source for the classification,
are generated using a Cartosat stereo scene. The classification is performed by using an algorithm for retrieving the terrain positioning
index. Different classification schemes are possible, therefore a set of input parameters is iteratively computed. The classification
results are then evaluated. For validating the classification stock data of an insurance are used. We compare the position of the reported
damages caused by extreme rain with our classification. By doing so we got the confirmation of the assumption.</p>
</abstract>
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