测绘通报 ›› 2018, Vol. 0 ›› Issue (5): 97-101,156.doi: 10.13474/j.cnki.11-2246.2018.0152

• 技术交流 • 上一篇    下一篇

土地利用遥感信息提取关键技术探讨

张正明1, 张志勋2, 常永青3, 王春4   

  1. 1. 江苏省测绘产品质量监督检验站, 江苏 南京 210013;
    2. 如皋市勘测院, 江苏 南通 226500;
    3. 南京市规划局, 江苏 南京 210029;
    4. 滁州学院, 安徽 滁州 239000
  • 收稿日期:2017-11-30 出版日期:2018-05-25 发布日期:2018-05-31
  • 作者简介:张正明(1963-),男,教授级高级工程师,主要研究方向为GIS信息处理和挖掘。E-mail:2285488459@qq.com

Research on Key Remote Sensing Information Extraction Technology of Land Use

ZHANG Zhengming1, ZHANG Zhixun2, CHANG Yongqing3, WANG Chun4   

  1. 1. Jiangsu Surveying and Mapping Product Quality Supervision and Inspection Station, Nanjing 210013, China;
    2. Rugao Survey Institute, Nantong 226500, China;
    3. Nanjing Urban Planning Bureau, Nanjing 210029, China;
    4. Chuzhou University, Chuzhou 239000, China
  • Received:2017-11-30 Online:2018-05-25 Published:2018-05-31

摘要:

针对传统土地利用解译技术的局限性,通过深入分析地物光谱特征,采用光谱角分类技术对一级地类进行分类,再根据光谱角影像和二级地类光谱特征构建分类规则,进行二级地类分类的分类方法。使用该方法对遥感影像进行遥感解译,并与监督分类中的最大似然法分类结果进行分类精度比较,结果表明,该方法的分类精度明显优于最大似然法分类,面积精度和空间精度都有明显提高,可以作为复杂地类的分类方法。

关键词: 遥感, 土地利用, 光谱角分类

Abstract:

To the limitations of traditional land use interpretation technologies,the spectral features of ground object were analyzed deeply.And a classification method using spectral angle classification technique to classify the first-level land types and according the spectral angle image and second-class spectral characteristics to build classification rules and classify the second-class spectral characteristics was proposed.This method was used to interpret remote sensing image,and the classification accuracy of this method's classification results were compared with the maximum likelihood classification in the supervised classification.The results show that the method's classification results had better classification accuracy than the maximum likelihood classification.Its area precision and spatial accuracy was improved obviously.And this method can be used as the classification method of complex ground object.

Key words: remote sensing, land use, spectral angle classification

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