华东师范大学学报(自然科学版) ›› 2005, Vol. 2005 ›› Issue (4): 59-65,8.

• 地理学 河口海岸学 • 上一篇    下一篇

ETM影像中城镇覆盖及其背景信息的提取方法研究

孟飞1,2，刘敏1，张心怡1   

  1. 1. 华东师范大学地理系，教育部地理信息科学重点实验室，上海 200062； 2.山东建筑工程学院 土木系，济南 250014
  • 收稿日期:2004-03-18 修回日期:2004-05-19 出版日期:2005-11-25 发布日期:2005-11-25
  • 通讯作者: 孟飞

Extracting Urban Cover and Background Information on ETM Images(Chinese)

MENG Fei 1, 2, LIU Min1 ， ZHANG Xin-yi1   

  1. 1. Department of geography, Key Laboratory of Geo-Information Sciences, East China Normal University, Shanghai 200062, China; 2.Department of Civil Engineering, Shandong Architecture & Engineering College, Jinan 250014, China
  • Received:2004-03-18 Revised:2004-05-19 Online:2005-11-25 Published:2005-11-25
  • Contact: MENG Fei

摘要： 通过分析国内相关文献所涉及的方法模型,对研究区各类地物谱间结构特征进行分析, 提出利用 TM4-TM7＜K1、TM5-TM4＞K2和 TM6＞K3三式联立提取出城镇覆盖信息．进一步探讨多步分类法：利用所提城镇覆盖信息掩膜原始图像去除城镇覆盖信息,对所得图像重新进行最优波段组合并继续分类．研究表明,所提模型能够更为有效地提取出城镇覆盖信息,普适性较前人有所提高,而分步提取方法则可较好地提取出土地利用信息．

关键词: 遥感图像, 城镇覆盖变化, 分步提取, 遥感图像, 城镇覆盖变化, 分步提取

Abstract: Owning to rapid economic development,land covers in urban areas,especially in Yangtze Delta, tend to change drastically in recent years.It is therefore,important to acquire the distribution of urban are- as and corresponding information.This study is focusing on the method of converting images into landuse covers from Landsat Enhanced Thematic Mapper(ETM).Firstly,it analyzes the mechanism of urban areas and other landuse types.Secondly,the model TM4-TM7＜K1,TM5-TM4＞K2 TM6＞K3 is introduced and proved to be better. Thirdly, multi-step-classification method, masking the original image with the urban thematic layer, thus to get rid of urban information and go on extracting the left landuse information with best bands combination, is introduced and discussed.The proposed method proves to have many advantages over the conventional supervised classification method and NDBI method. It is more objective and faster than supervised classification method and can acquire a higher accuracy compared with NDBI method.

Key words: urban area, multiple-stage-classification, Shanghai, Classification model, urban area, multiple-stage-classification, Shanghai

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