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Position: Home > Articles > Classification Method for ETM+Remote Sensing Images during Extraction of Vegetation Information Jiangsu Journal of Agricultural Sciences 2008,24 (1) 29-32

植被信息提取过程中ETM+遥感影像的分类方法

作  者:
何瑞银;沈明霞;从静华
单  位:
南京农业大学工学院;南京森林公安高等专科学校
关键词:
遥感影像;非监督分类;植被信息
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。
译  名:
Classification Method for ETM+Remote Sensing Images during Extraction of Vegetation Information
作  者:
HE Rui-yin1,SHEN Ming-xia1,CONG Jing-hua2 (1.College of Engineering,Nanjing Agricultural University,Nanjing 210031,China;2.Nanjing Forest Police College,Nanjing 210046,China)
关键词:
remote sensing image;unsupervised classification;vegetation information
摘  要:
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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