ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 107-111, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
18 Jul 2012
U. Michel1, H. Thunig1, M. Ehlers2, and P. Reinartz3 1University of Education Heidelberg, Department for Geography, Czernyring 22/11-12, 69115 Heidelberg, Germany
2University Osnabrück, Institut für Geoinformatik und Fernerkundung, Barbarastraße 22b, 49076 Osnabrück, Germany
3Deutsches Zentrum für Luft- und Raumfahrt e.V., Münchner Straße 20, 82234 Oberpfaffenhofen-Wessling, Germany
Keywords: Change Detection, Disaster Management, Classification, Segmentation, Monitoring, Knowledge Abstract. This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.
Conference paper (PDF, 1874 KB)

Citation: Michel, U., Thunig, H., Ehlers, M., and Reinartz, P.: RAPID CHANGE DETECTION ALGORITHM FOR DISASTER MANAGEMENT, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 107-111,, 2012.

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