黄土滑坡高分辨率遥感影像识别——以陕西省延安市地区为例

丁辉; 张茂省; 朱卫红; 张涛

doi:10.19751/j.cnki.61-1149/p.2019.03.022

黄土滑坡高分辨率遥感影像识别——以陕西省延安市地区为例

丁辉^1,2,
张茂省^1,2,
朱卫红¹,
张涛¹

1. 中国地质调查局西安地质调查中心/西北地质科技创新中心, 陕西西安 710054;
2. 自然资源部黄土地质灾害重点实验室, 陕西西安 710054

基金项目:

国家自然科学基金项目“基于多特征面向对象黄土滑坡现象识别”（41502338）、“黄土水敏性力学机制及其致滑机理研究”（41530640），中国地质调查局地质大调查项目“关中平原城市群综合地质调查”（DD20190294）共同资助

详细信息

作者简介:
丁辉(1975-),女,博士,主要研究方向为资源与环境遥感、地质灾害区划。E-mail:xagscdh@126.com

中图分类号: P627
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出版历程
- 收稿日期: 2018-09-17
- 修回日期: 2019-03-27
- 网络出版日期: 2022-07-28
- 发布日期: 2019-09-04

High Resolution Remote Sensing for the Identification of Loess Landslides: Example from Yan'an City

1. Xi'an Center of China Geological Survey/Northwest China Center for Geoscience Innovation, Xi'an 710054, Shaanxi, China;
2. Key Laboratory for Geo-hazards in Loess Area, MNR, Xi'an 710054, Shaanxi, China

摘要

摘要: 中国西部黄土高原地区面积广，滑坡数量多。针对野外调查和目视解译费时、费力、周期较长等问题，利用高空间分辨率卫星遥感数据和DEM，以中国陕西省延安市宝塔区为研究区域，采用面向对象的分类方法，基于波段选择、尺度分析，利用影像分割，结合光谱特征、空间特征和地貌特征进行区域黄土滑坡识别。实验区分析结果表明，滑坡后壁和滑坡体识别精度达78.9%和73.6%，滑坡后壁比滑坡体更易于识别，该方法对研究同类型区域滑坡编目、地学分析和影像理解具有重要的意义。
- 黄土滑坡 /
- 面向对象分类 /
- 集合 /
- 多特征 /
- 编目 /
- 影像理解
Abstract: The Loess Plateau in West China covers a wide area and has many landslides. Due to problems with field investigations and visual interpretation, such as the time and effort involved, as well as relatively long cycles of data acquisition and update, the high spatial resolution remote sensing data and digital elevation model (DEM) have been used for regional identification of loess landslides in Baota District, Yan'an City, Shaanxi Province, China. The regional loess landslides have been identified by using the object-oriented classification method, this method is to use the image segmentation combined with spectral-spatial and geomorphological features and based on band selection and scale analysis to analyze the landslides. Analysis of the study site showed that the recognition accuracy for the landslide back scarp and landslide mass is 78.9% and 73.6%, respectively; the landslide back scarp is easier to recognize than the landslide mass. This method is important for landslide cataloging, earth science analysis, and image interpretation.
- loess landslides /
- object-oriented classification /
- set /
- multiple features /
- landslide cataloging

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