文章基本信息

标题：Earthquake Destruction Assessment of Urban Roads Network Using Satellite Imagery and Fuzzy Inference Systems
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作者：F. Samadzadegan ; N. Zarrinpanjeh
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII-B8
页码：409-414
出版社：Copernicus Publications
摘要：The idea of assessing the damages to roads network after earthquake strikes has been referred as a matter of importance for performing every systematically planned activity in the affected region. When gathering information about the devastated region is crucially costly and time-consuming and in most cases impossible, high-resolution satellite imagery is hired to provide fast and accurate information for every assessment and evaluation processes. Therefore, this research is mainly focused on design and development of a method to evaluate the damages to the roads network, using pre-event digital vector map and post-event high- resolution satellite imagery. In this case, using pre-event digital vector map and post-event satellite imagery, the roads network is extracted and then processed for determination of road blocks across city roads network. The existence of many violating objects at the scene of satellite imagery may mislead the process of finding blocked roads. Therefore, violating objects are detected and removed through Fuzzy engines. Then, analyzing the spectral and textural properties and comparing specific computed set of descriptors, the damage and no-damage objects are marked. Through inspecting the results, hiring Fuzzy Inference Systems, the blocked road sections in the region are detected. The proposed method is evaluated using digital vector map and QuickBird Pan- sharpened images of the city of Bam, located southwest of Iran regarding to the devastating earthquake in December 2003. The visual inspections have confirmed the capabilities of the method for evaluation urban road network after earthquake strikes
关键词：Satellite Remote Sensing; Damage Assessment; Computer Vision; Texture Analysis; Disaster Management; Fuzzy ; Inference Systems