Abstract
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.
Highlights
Change detection analyses are known as useful methods in a wide field of applications where two images of the same area taken at two or more different time steps were compared in order to identify changes (Radke et al, 2005)
This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects
This paper focuses on rapid change detection which is useful for several applications like monitoring of ongoing war actions, monitoring of new building constructions and urban growth and detection of affected areas for disaster management
Summary
Change detection analyses are known as useful methods in a wide field of applications where two images of the same area taken at two or more different time steps were compared in order to identify changes (Radke et al, 2005). Postclassification comparison and principal component analysis are popular methods in change detection studies. During last decades artificial neural networks and spectral mixture analysis were carried out as feasible methods for change detection as well. An overview of the typical change detection methods could be found at Jianya et al (2008) and Lu et al (2004). In recent years change detection methods become applicable in particular to the field of disaster management (Günthert et al, 2011; Al-Khudhairy, Caravaggi, 2005)
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