测绘通报 ›› 2018, Vol. 0 ›› Issue (2): 67-71,82.doi: 10.13474/j.cnki.11-2246.2018.0046

• 行业观察 • 上一篇    下一篇

高分一号汶川极震区滑坡提取研究

黄汀1, 白仙富2, 庄齐枫1, 徐敬海1   

  1. 1. 南京工业大学测绘科学与技术学院, 江苏 南京 210009;
    2. 云南省地震局, 云南 昆明 650000
  • 收稿日期:2017-05-16 修回日期:2017-07-23 出版日期:2018-02-25 发布日期:2018-03-06
  • 作者简介:黄汀(1992-),男,硕士生,主要从事遥感技术与应用工作。E-mail:huangting_77991@163.com
  • 基金资助:

    中国地震局工程力学研究所基本科业务费专项资助项目(2016QJGJ09);国家自然科学基金(40901272);江苏省测绘地理信息科研项目(JSCHKY201506);空间信息智能感知与服务深圳市重点实验室(深圳大学)开放基金(201404)

Research on Landslides Extraction Based on the Wenchuan Earthquake in GF-1 Remote Sensing Image

HUANG Ting1, BAI Xianfu2, ZHUANG Qifeng1, XU Jinghai1   

  1. 1. Institute of Mapping Science and Technology, Nanjing Tech University, Nanjing 210009, China;
    2. Seismological Bureau of Yunnan Province, Kunming 650000, China
  • Received:2017-05-16 Revised:2017-07-23 Online:2018-02-25 Published:2018-03-06

摘要:

选择汶川地震极震区的高分一号卫星影像,通过面向对象的分析技术提取滑坡信息;采用多尺度分割算法,结合高分影像和滑坡特点将以往经验式参数选取方法进行优化,分析极震区滑坡的特征,选择合适的特征参数,构建分类规则,实现滑坡的识别与提取。滑坡灾害信息的提取结果采用野外实际调查的滑坡点进行精度评价,滑坡提取总精度为84%,表明利用高分一号高分辨率卫星数据可以较好地提取滑坡灾害信息,基本满足滑坡灾害识别的要求。

关键词: 高分一号, 面向对象, 多尺度分割, 滑坡提取

Abstract:

Earthquake induced landslide is the most common and destructive type of earthquake, so it is of great significance to quickly and accurately obtain the distribution of landslides after the earthquake. The traditional method of artificial interpretation of remote sensing is of low efficiency. In this paper, we select the GF-1 image of the Wenchuan earthquake zone and extract the landslide information by using the object-oriented method. Multi-scale segmentation is used to set the different segmentation parameters. The suitable parameters are selected according to the features of spectrum, texture and geometry, and the classification rules are constructed to extract the landslide area. Through the comparison of landslide with landslide points by actual investigating, the accuracy of object-oriented classification method for 84%. The result shows that GF-1 data can be used to extract information of landslide hazard and meet the basic requirements of landslide hazard identification.

Key words: GF-1, object-oriented, multi-scale segmentation, landslide extraction

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