Abstract
Abstract. The experiences from recent disaster events showed that detailed information derived from high-resolution satellite images could accommodate the requirements from damage analysts and disaster management practitioners. Richer information contained in such high-resolution images, however, increases the complexity of image analysis. As a result, few image analysis solutions can be practically used under time pressure in the context of post-disaster and emergency responses. To fill the gap in employment of remote sensing in disaster response, this research develops a rapid high-resolution satellite mapping solution built upon a dual-scale contextual framework to support damage estimation after a catastrophe. The target objects are building (or building blocks) and their condition. On the coarse processing level, statistical region merging deployed to group pixels into a number of coarse clusters. Based on majority rule of vegetation index, water and shadow index, it is possible to eliminate the irrelevant clusters. The remaining clusters likely consist of building structures and others. On the fine processing level details, within each considering clusters, smaller objects are formed using morphological analysis. Numerous indicators including spectral, textural and shape indices are computed to be used in a rule-based object classification. Computation time of raster-based analysis highly depends on the image size or number of processed pixels in order words. Breaking into 2 level processing helps to reduce the processed number of pixels and the redundancy of processing irrelevant information. In addition, it allows a data- and tasks- based parallel implementation. The performance is demonstrated with QuickBird images captured a disaster-affected area of Phanga, Thailand by the 2004 Indian Ocean tsunami are used for demonstration of the performance. The developed solution will be implemented in different platforms as well as a web processing service for operational uses.
Highlights
Providing quick and reliable captured information in hardest hit and difficult-to-assess areas, remote sensing products have been commonly used as the first and primary information source at the post-disaster response stage (Adams et al, 2004; Balz and Liao, 2010; Matsuoka and Yamazaki 1999; Saito et al, 2004; Stramondo et al, 2006; Vu et al 2005)
Richer information contained in such high-resolution images increases the complexity of image analysis
Ban Nam Ken village, one of the most affected areas due to the 2004 Indian Ocean tsunami was selected as the study area with a QuickBird image captured on 2 January 2005, about a week after the tsunami attack
Summary
Providing quick and reliable captured information in hardest hit and difficult-to-assess areas, remote sensing products have been commonly used as the first and primary information source at the post-disaster response stage (Adams et al, 2004; Balz and Liao, 2010; Matsuoka and Yamazaki 1999; Saito et al, 2004; Stramondo et al, 2006; Vu et al 2005). Researches in response to recent disaster events showed that detailed information derived from very high-resolution satellite images could accommodate their requirements (Chesnel et al 2007; Gusella et al 2005; Saito et al 2004; Vu and Ban, 2010)
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