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遥感技术与应用  2010, Vol. 25 Issue (1): 149-154    DOI: 10.11873/j.issn.1004-0323.2010.1.149
图像处理     
基于多层分割的面向对象遥感影像分类方法研究
彭海涛,柯长青
南京大学地理与海洋科学学院,江苏 南京 210093)
Study on Object-oriented Remote Sensing Image Classification Based on Multi-levels Segmentation
PENG Hai-tao, KE Chang-qing
School of Geographic and Oceanographic Sciences,Nanjing University,Nanjing 210093,China
 全文: PDF(3202 KB)  
摘要:

利用ALOS数据,在Definiens Developer 7软件中用分形网络演化法(FNEA)进行多级分割,获取影像对象。综合运用对象的光谱、空间特征和不同层对象之间的关系,提取了湖北省洪湖市试验区土地覆盖与土地利用信息。最后,用一种基于单层分割的面向对象分类方法和基于像素的最大似然法与这种基于多级分割的面向对象分类方法进行了对比分析。结果表明,基于多级分割的面向对象分类方法,不仅克服了基于像素的最大似然法出现的“椒盐”现象,在分类精度上较这两种分类方法也有大幅度的提高。

关键词: 面向对象多级分割模糊函数分类ALOS影像    
Abstract:

ALOS image data was used to carry out a multi-levels segmentation with a method called FNEA in Definiens Developer 7 solftware and image objects were got.Spectral and spatial values of image objects,as well as relationship of objects among different levels were considered to extract land use and land cover information in the test area located in Honghu City,Hubei Province.Then an object-oriented classification based on single level segmentation and a pixel-based Maximum Likelihood classification were used to compare with it.Results showed that the object-oriented classification based on multi-levels segmentation not only overcame “Pepper and Salt Phenomenon” appeared in the pixel-based Maximum Likelihood classification but also obtained a significant improvement on classification accuracy compared with the other two classification methods.

Key words: Object-oriented    Multi-levels segmentation    Fuzzy function    Classification    ALOS image
收稿日期: 2009-05-26 出版日期: 2011-11-04
基金资助:

国家自然科学基金重点项目(40730635),水利部公益项目(200701024)资助。

作者简介: 彭海涛(1984-),男,硕士研究生,主要从事遥感图像处理与遥感应用研究。E-mail:sdptao@sina.com。
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引用本文:

彭海涛, 柯长青. 基于多层分割的面向对象遥感影像分类方法研究[J]. 遥感技术与应用, 2010, 25(1): 149-154.

PENG Hai-tao, KE Chang-qing. Study on Object-oriented Remote Sensing Image Classification Based on Multi-levels Segmentation. Remote Sensing Technology and Application, 2010, 25(1): 149-154.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2010.1.149        http://www.rsta.ac.cn/CN/Y2010/V25/I1/149

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