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6 August 2015 A rapid extraction of landslide disaster information research based on GF-1 image
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Proceedings Volume 9669, Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China; 96690C (2015) https://doi.org/10.1117/12.2204784
Event: Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China, 2014, Xian City, China
Abstract
In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 ㎡.Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.
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Sai Wang, Suning Xu, Ling Peng, Zhiyi Wang, and Na Wang "A rapid extraction of landslide disaster information research based on GF-1 image", Proc. SPIE 9669, Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China, 96690C (6 August 2015); https://doi.org/10.1117/12.2204784
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