测绘通报 ›› 2019, Vol. 0 ›› Issue (11): 134-136,159.doi: 10.13474/j.cnki.11-2246.2019.0367

• 技术交流 • 上一篇    下一篇

遥感影像与POI数据相结合的城市建成区提取适用性研究——以沈阳市为例

武新宇, 孙立双, 谢志伟, 张丹华, 于莉莉   

  1. 沈阳建筑大学交通工程学院, 辽宁 沈阳 110168
  • 收稿日期:2019-03-11 发布日期:2019-12-02
  • 通讯作者: 谢志伟。E-mail:zwxrs16@163.com E-mail:zwxrs16@163.com
  • 作者简介:武新宇(1995-),男,硕士生,主要研究方向为城市空间地理大数据分析。E-mail:wuxinyu127@163.com
  • 基金资助:
    辽宁省科学技术计划(2017231008)

Applicability of urban built-up area extraction based on remote sensing image and POI data: a case study of Shenyang

WU Xinyu, SUN Lishuang, XIE Zhiwei, ZHANG Danhua, YU Lili   

  1. School of Transportation Engineering, Shenyang Jianzhu University, Shenyang 110168, China
  • Received:2019-03-11 Published:2019-12-02

摘要: 城市建成区的发展状况是地理国情监测的重要内容,本文基于遥感影像数据和POI数据对城市建成区进行提取,针对二者的适用性问题进行了研究。试验以沈阳市为研究区域,在研究区域内选择2016年遥感影像数据和POI数据作为数据源进行对比分析。首先,对遥感影像数据和POI数据进行预处理;其次,通过监督分类的方法对遥感影像进行建成区的提取;然后,采用核密度估计法分析POI数据并提取出建成区;最后,利用叠加分析法对比分析这两种数据的适用性。试验结果表明:使用遥感影像数据作为数据源可以较为全面客观地反映城市建成区的发展现状;利用POI数据提取出的城市建成区具有较强的经济属性,能够很好地反映出城市中的经济活跃区。

关键词: 城市建成区, POI, 监督分类, 核密度分析, 提取

Abstract: The development status of urban built-up areas is an important part of geographic condition monitoring. This paper extracts urban built-up areas based on remote sensing image data and POI data, and studies their applicability. Shenyang is chosen as the research area, and 2016 remote sensing image data and POI data are selected as data sources for comparative study. Firstly, the remote sensing image data and POI data are preprocessed. Secondly, the built-up area of remote sensing image is extracted by supervised classification method. Then, the POI data are analyzed by kernel density estimation method and the built-up area is extracted. Finally, the applicability of these two data is compared and analyzed by superposition analysis method. The results show that using remote sensing image data as data source can reflect the development status of urban built-up areas comprehensively and objectively, and the urban built-up areas extracted from POI data have strong economic attributes, which can well reflect the economic active areas in cities.

Key words: urban built-up area, point of interest, supervised classification, kernel density estimation, extraction

中图分类号: