A New Method for Urban Change Detection and Its Application in Changsha Urban Area

MO Hong-wei; LI Shao-qing; YUAN Zhi-fen; TANG Shuang; WANG Xin

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A New Method for Urban Change Detection and Its Application in Changsha Urban Area

- A New Method for Urban Change Detection and Its Application in Changsha Urban Area
- Bulletin of Soiland Water Conservation Vol. 31, Issue 4, Pages: 134-138(2012)
- 作者机构：
  
  1. 湖南科技大学地球空间信息科学研究中心,湖南,湘潭,411201
  2. 中国科学院寒区旱区资源与工程研究所,甘肃,兰州,730000
- 作者简介：
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- Published：2012
- 稿件说明：
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MO Hong-wei, LI Shao-qing, YUAN Zhi-fen, et al. A New Method for Urban Change Detection and Its Application in Changsha Urban Area[J]. Bulletin of Soiland Water Conservation, 2012, 31(4): 134-138.
DOI：

MO Hong-wei, LI Shao-qing, YUAN Zhi-fen, et al. A New Method for Urban Change Detection and Its Application in Changsha Urban Area[J]. Bulletin of Soiland Water Conservation, 2012, 31(4): 134-138. DOI：

摘要

城市变化检测是遥感应用的重要领域．以长沙都市区为例，利用两期Landsat TM影像，改进了移动窗口法，提出了差分图像异常检测法．结果显示：(1)差分图像异常检测法有较高的检测精度．在随机选取的178个变化/非变化像元中，有157个被正确检测到，精度达88.2%；(2)2004—2009年间，长沙都市区城市变化像元共计116 101个，即该区有5.41%的自然景观转化为城市景观．园区经济的快速发展成为该区城市扩张的主要驱动力．

Abstract

Urban change detection is an important field of remote sensing applications. This study presents a method for detecting urban change from remotely sensed data. We proposed the differential image anomaly detection method to improve the moving window method using the Landsat TM data. Changsha urban area was chosen as an example to illustrate the method. The results show that the differential image anomaly de-tection method has higher detection accuracy~ 157 out of 178(88.2%) randomly selected pixels were correct-ly detected. In Changsha urban area

116 101(5.41%) pixels which was natural landscape turned into urban landscape during 2004-2009. Economic development of industrial areas became the main driving force of the urban change in the study area.

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