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Object-oriented urban dynamic monitoring — A case study of Haidian District of Beijing

机译:面向对象的城市动态监测-以北京市海淀区为例

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摘要

It is crucial to conduct the land use/cover research to obtain the global change information. Urban area is one of the most sensitive areas in land use/cover change. Therefore land use/cover change in urban areas is very important in global change. It is vital to incorporate the information of urban land use/cover change into the process of decision-making about urban area development. In this paper, a new urban change detection approach, urban dynamic monitoring based on objects, is introduced. This approach includes four steps: 1) producing multi-scale objects from multi-temporal remotely sensed images with spectrum, texture and context information; 2) extracting possible changed objects adopting object-oriented classification; 3) obtaining shared objects as the basic units for urban change detection; 4) determining the threshold to segment the changed objects from the possible changed objects using Otsu method. In this paper, the object-based approach was applied to detecting the urban expansion in Haidian District, Beijing, China with two Landsat Thematic Mapper (TM) data in 1997 and 2004. The results indicated that the overall accuracy was about 84.83%, and Kappa about 0.785. Compared with other conventional approaches, the object-based approach was advantageous in reducing the error accumulation of image classification of each datum and in independence to the radiometric correction and image registration accuracy.
机译:进行土地使用/覆盖研究以获得全球变化信息至关重要。市区是土地利用/覆盖变化中最敏感的地区之一。因此,城市地区的土地利用/覆盖变化在全球变化中非常重要。将城市土地使用/覆盖变化的信息纳入有关城市区域发展的决策过程至关重要。本文介绍了一种新的城市变化检测方法,即基于对象的城市动态监测。该方法包括四个步骤:1)从具有光谱,纹理和上下文信息的多时间遥感图像中生成多尺度对象; 2)采用面向对象分类法提取可能的变化对象; 3)获取共享对象作为城市变化检测的基本单位; 4)确定阈值,以使用Otsu方法从可能的更改对象中分割更改的对象。本文采用基于对象的方法,利用1997年和2004年的两个Landsat Thematic Mapper(TM)数据,检测了北京市海淀区的城市扩展情况。结果表明,总体准确度约为84.83%,并且卡伯约0.785。与其他常规方法相比,基于对象的方法在减少每个基准的图像分类的误差累积以及独立于辐射校正和图像配准精度方面具有优势。

著录项

  • 来源
    《Chinese Geographical Science》 |2007年第3期|236-242|共7页
  • 作者

    Kai An; Jinshui Zhang; Yu Xiao;

  • 作者单位

    Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China;

    Key Laboratory of Environmental Change and Natural Disaster Ministry of Education of China Beijing Normal University Beijing 100875 China;

    Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    urban change detection; object-oriented method; remote sensing; land use/cover; Otsu method;

    机译:城市变化检测;面向对象方法;遥感;土地利用/覆盖;大津法;

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