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首页> 外文期刊>Natural Hazards and Earth System Sciences Discussions >Co-seismic surface effects from very high resolution panchromatic images: the case of the 2005 Kashmir (Pakistan) earthquake
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Co-seismic surface effects from very high resolution panchromatic images: the case of the 2005 Kashmir (Pakistan) earthquake

机译:来自非常高分辨率的共分辨率的共同地震表面效应:2005年克什米尔(巴基斯坦)地震的情况

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The use of Very High Resolution (VHR) satellite panchromatic image is nowadays an effective tool to detect and investigate surface effects of natural disasters. We specifically examined the capabilities of VHR images to analyse earthquake features and detect changes based on the combination of visual inspection and automatic classification tools. In particular, we have used Quickbird (0.6 m spatial resolution) images for detecting the three main co-seismic surface features: damages, ruptures and landslides. The present approach has been applied to the 8 October 2005, Mw7.6 Kashmir, Pakistan, earthquake. We have focused our study in and around the main urban areas hit by the above earthquake specifically at Muzaffarabad and Balakot towns. The automatic classification techniques provided the best results wherever dealing with the damage to man-made structures and landslides. On the other hand, the visual inspection method demonstrated in addressing the identification of rupture traces and associated features. The synoptic view (concerning landslide, more than 190 millions of pixels have been automatically classified), the spatiotemporal sampling and the fast automatic damage detection using satellite images provided a reliable contribution to the prompt response during natural disaster and for the evaluation of seismic hazard as well.
机译:如今,使用非常高分辨率(VHR)卫星全景图像是一种检测和研究自然灾害的表面影响的有效工具。我们专门检查了VHR图像的能力,以分析地震特征,并根据目视检查和自动分类工具的组合检测变化。特别是,我们使用了QuickBird(0.6米空间分辨率)图像来检测三个主要的共同地震表面特征:损坏,破裂和山体滑坡。目前的方法已应用于2005年10月8日,MW7.6克什米尔,巴基斯坦,地震。我们专注于主要城市地区的研究,在Muzaffarabad和Balakot城镇的上述地震中受到了上述地震的影响。自动分类技术提供了最好的结果,尽最大效果,以便处理对人造结构和山体滑坡的损坏。另一方面,目视检查方法在解决破裂迹线和相关特征的识别方面说明。 SYNOPTIC视图(关于滑坡,超过190百万像素自动分类),使用卫星图像的时空采样和快速自动损坏检测为自然灾害期间的迅速响应提供了可靠的贡献,以及评估地震危险出色地。

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