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Earthquake-induced geological hazards detection under hierarchical stripping classification framework in the Beichuan area

机译:分层剥离分类框架下北川地区地震诱发地质灾害探测

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摘要

The Wenchuan earthquake-induced geological hazards, such as rock falls, landslides, and debris flows, caused as much damage as the earthquake itself. In the complicated environment of the disaster area, seldom can a single remotely sensed imagery and a single classification method meet the needs of the interpretation task. In this paper, taking the Beichuan area as an example, the hierarchical stripping classification (HSC) framework, supported by human-computer interactive interpretation, was applied to tackle the problems caused by the earthquake, as well as the local complicated landform and weather environments. Different classification tools were used to detect specific objects under the HSC framework. Multi-source and multi-temporal data from satellite remote sensors (mainly CBERS-02B charge-coupled device and HR images), unmanned aerial vehicles (UAV), digital elevation model, and other auxiliary sources were utilized to detect the distribution of geological hazards. The classification results were compared with that of an overall supervised classification. The results show that the HSC framework and the related methods are effective and work well in the study area.
机译:汶川地震引起的地质灾害,如岩崩,滑坡和泥石流,造成的破坏与地震本身一样多。在灾区复杂的环境中,很少有单一的遥感影像和单一的分类方法能够满足口译任务的需要。本文以北川地区为例,采用人机交互解释支持的分层剥离分类(HSC)框架,解决了地震,局部复杂地形和天气环境等问题。 。在HSC框架下,使用了不同的分类工具来检测特定对象。来自卫星遥感器(主要是CBERS-02B电荷耦合设备和HR图像),无人机(UAV),数字高程模型和其他辅助源的多源和多时数据被用于检测地质灾害的分布。将分类结果与整体监督分类的结果进行比较。结果表明,HSC框架和相关方法是有效的,并且在研究领域中行之有效。

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