| 植被信息提取过程中ETM+遥感影像的分类方法 |
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| 引用本文： | 何瑞银,沈明霞,从静华. 植被信息提取过程中ETM+遥感影像的分类方法[J]. 江苏农业学报, 2008, 24(1): 29-32 |
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| 作者姓名： | 何瑞银 沈明霞 从静华 |
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| 作者单位： | 1. 南京农业大学工学院,江苏,南京,210031
2. 南京森林公安高等专科学校,江苏,南京,210046 |
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| 基金项目： | 江苏省科技厅高技术研究项目(BG2005328) |
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| 摘 要： | 遥感图像分类是研究土地利用覆盖变化的基础。本文以南京市为例,采用监督分类与非监督分类相结合的方法,先以ISODATA非监督分类法获得初始训练模板,通过实地调查对模板进行调整,再在此基础上以最大似然法进行监督分类。研究结果表明,用改进的分类法提取植被信息,可以取得良好的分类效果,是进行植被信息调查较为理想的方法。
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| 关 键 词： | 遥感影像 非监督分类 植被信息 |
| 文章编号： | 1000-4440（2008）01-0029-04 |
| 收稿时间： | 2007-04-07 |
| 修稿时间： | 2007-04-07 |
Classification Method for ETM+Remote Sensing Images during Extraction of Vegetation Information |
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| HE Rui-yin,SHEN Ming-xia,CONG Jing-hua. Classification Method for ETM+Remote Sensing Images during Extraction of Vegetation Information[J]. Jiangsu Journal of Agricultural Sciences, 2008, 24(1): 29-32 |
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| Authors: | HE Rui-yin SHEN Ming-xia CONG Jing-hua |
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| Abstract: | Remote sensing image classification is a research foundation of land utilization and vegetation change.To develop an optimum classification method for remote sensing image during the extraction of vegetation information,Nanjing city was taken as an example.Initial training template was deduced by ISODATA unsupervised classification and was verified by survey in reality.The method of maximum likelihood was employed in the supervised classification of these images.The research results showed that the improved method,integrating the supervised and unsupervised classification,is considerably ideal for the vegetation information investigation. |
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| Keywords: | remote sensing image unsupervised classification vegetation information |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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