Earthquake-induced landslide recognition using high-resolution remote sensing images

Ling PENG; Suning XU; Junjun MEI; Fenghuan SU

doi:10.11834/jrs.20176176

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Earthquake-induced landslide recognition using high-resolution remote sensing images
Vol. 21, Issue 4, Pages: 509-518(2017)
Received：30 May 2016，

Accepted：04 February 2017，

Published：2017-07
DOI： 10.11834/jrs.20176176
稿件说明：

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彭令, 徐素宁, 梅军军, 苏凤环. 2017. 地震滑坡高分辨率遥感影像识别. 遥感学报, 21(4): 509–518 DOI： 10.11834/jrs.20176176.

Peng L, Xu S N, Mei J J and Su F H. 2017. Earthquake-induced landslide recognition using high-resolution remote sensing images. Journal of Remote Sensing, 21(4): 509–518 DOI： 10.11834/jrs.20176176.

摘要

区域性地震滑坡信息获取目前主要通过遥感目视解译和计算机提取，存在主观性强、耗时费力、提取精度低等问题，导致难以满足灾后应急调查、灾情评估等方面的应用需求。采用资源三号、高分一号高分辨率遥感影像，以汶川震区为实验区，在地震滑坡灾害特征分析的基础上，通过多尺度最优分割方法构建多层次滑坡对象，融合光谱、纹理、几何等影像特征和地形特征信息建立多维滑坡识别规则集合，基于高分辨率影像认知模式与场景理解过程提出滑坡分层识别模型，从而实现地震滑坡空间分布及其滑源区、滑移区和堆积区的准确识别。实验区分析结果显示最低识别精度为81.89%，而滑坡的堆积区最容易被分辨，识别方法具有可推广性。研究成果可为灾后应急调查提供技术支撑，并促进国产高分辨率遥感卫星的地质灾害应用。

Abstract

Earthquake-induced landslides are the most common geological disasters caused by large seismic activities in mountainous areas

and they are known for their suddenness

destructiveness

and extensive distribution range. These landslides often result in severe casualties and economic losses. Currently

regional earthquake-induced landslides are mainly obtained by visual interpretation and computer data extraction from remote sensing images. These methods are objective

time-consuming

and low in precision. Thus

they cannot address the requirement of practically conducting emergency surveys and disaster evaluations after earthquakes. In this study

with the main data source of high-resolution remote sensing images from ZY-3 and GF-1

as well as the study area of the Wenchuan earthquake region

objects of multilevel landslides were established using the multi-scale optimum partition method based on an in-depth analysis of seismic landslide features. A recognition rule set of multi-dimensional landslides was also built by combining topographic and image features

such as spectrum

texture

and geometry. Additionally

recognition models for landslide stratification were proposed based on the recognition models of high-resolution images and an understanding of the scenes. Through all of the preceding efforts mentioned

the spatial distribution of the seismic landslide

as well as the sliding source

transport

and depositional areas

can be identified. The analysis results of the experimental area showed a minimum recognition accuracy of 81.89%

with the depositional zone of landslides being the easiest zone to recognize

and the established method can be generalized. These findings may provide technical support for post-earthquake emergency investigations and further promote the application of high-resolution remote sensing data from Chinese satellites for landslides recognition.

关键词

Keywords

references

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