利用面向对象分类技术的大尺度土地覆被调查方法 |
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作者姓名: | 罗开盛 李仁东 常变蓉 |
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作者单位: | 1. 中国科学院测量与地球物理研究所, 武汉 430077;
2. 中国科学院大学, 北京 100049;
3. 湖北省环境与灾害监测评估重点实验室, 武汉 430077 |
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基金项目: | 中国科学院战略性先导科技专项(XDA0505107)资助 |
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摘 要: | 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.
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关 键 词: | HJ-CCD影像 面向对象 大尺度 邻域推移分类法 ecognition |
收稿时间: | 2013-03-01 |
修稿时间: | 2013-03-20 |
Land-cover survey method using object-oriented technology and HJ-CCD image on large scale |
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Authors: | LUO Kai-Sheng LI Ren-Dong CHANG Bian-Rong |
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Institution: | 1. Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;
2. University of Chinese Academy Sciences, Beijing 100049, China;
3. Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei Province, Wuhan 430077, China |
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Abstract: | 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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Keywords: | HJ remote sensing image object-oriented large scale partition mobile classification method ecognition |
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