Volume XL-7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 93-100, 2014
https://doi.org/10.5194/isprsarchives-XL-7-93-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 93-100, 2014
https://doi.org/10.5194/isprsarchives-XL-7-93-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Sep 2014

19 Sep 2014

Automatic surface classification for retrieving areas which are highly endangered by extreme rain

P. Fischer1, T. Krauß1, and T. Peters2 P. Fischer et al.
  • 1Remote Sensing Technology Institute, German Aerospace Center (DLR), Münchener Str. 20, 82234 Wessling, Germany
  • 2Westfälische Provinzial Versicherung AG, Provinzial-Allee 1, 48181 Münster, Germany

Keywords: Automatic DSM classification, Terrain Positioning Index, extreme rain, damage probability

Abstract. 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.