收稿日期: 2013-03-01
修回日期: 2013-03-20
网络出版日期: 2013-11-15
基金资助
中国科学院战略性先导科技专项(XDA0505107)资助
Land-cover survey method using object-oriented technology and HJ-CCD image on large scale
Received date: 2013-03-01
Revised date: 2013-03-20
Online published: 2013-11-15
以HJ-CCD为实验数据,采用面向对象分类技术,对地形复杂、类型多样的湖南省进行土地覆盖类型的自动提取.着重研究在大尺度上的土地覆被调查中应用HJA/B遥感影像和面向对象技术获取土地覆被信息的一整套技术方法.将多尺度分割、邻域推移分类法以及野外调查、专家知识有机结合起来,并用野外采样点进行精度检验.湖南省土地覆被调查结果的总体精度84.99%,Kappa系数为82.79%.结果证明了以HJ-CCD影像为遥感数据源,利用面向对象技术进行大尺度的土地覆被调查的可行性和有效性.
关键词： HJ-CCD影像; 面向对象; 大尺度; 邻域推移分类法; ecognition
罗开盛 , 李仁东 , 常变蓉 . 利用面向对象分类技术的大尺度土地覆被调查方法[J]. 中国科学院大学学报, 2013 , 30(6) : 770 -778 . DOI: 10.7523/j.issn.2095-6134.2013.06.009
Based on HJ-CCD remote image data, object-oriented technology was applied into the land-cover extraction in Hunan Province. We mainly focus on land-cover survey method using HJA/B remote sensing images on large scale and explore a set of approaches to get land-cover information using object-oriented technology. The overall accuracy of the results is 84.99% and Kappa coefficient is 82.79%. The research results show that land-cover survey method using HJ-CCD remote sensing images and object-oriented classification technology is feasible and effective on large scale.
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