Vegetation Classification Model Based on High-resolution Satellite Imagery

机译：基于高分辨率卫星影像的植被分类模型

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摘要

Based on a SPOT-5 image, this study built knowledge pool of vegetation spectral information, adopted classification algorithm of decision tree, proposed a vegetation classification model based on their spectral information and classified the vegetation of Nanjing. The results showed that the overall accuracy was 86.95% and Kappa coefficient was 0.8287. Then the classification model was validated by using an IKONOS image of Yuhuatai region and was improved through combining the textural information. The classification overall accuracy was increased to 92.70% and Kappa coefficient was increased to 0.8648.

机译：基于SPOT-5图像，建立了植被光谱信息知识库，采用决策树分类算法，提出了基于光谱信息的植被分类模型，对南京地区的植被进行了分类。结果表明，总体准确度为86.95％，卡伯系数为0.8287。然后，使用雨花台地区的IKONOS图像对分类模型进行验证，并结合纹理信息对其进行改进。分类的整体准确性提高到92.70％，卡伯系数提高到0.8648。

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来源
《Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China》|2005年|P.62000F.1-62000F.9|共9页
会议地点 Guiyang City(CN)
作者
Chen Junying; Tian Qingjiu;
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作者单位

Key laboratory of China Remote Sensing Satellite Ground Station, Chinese Academy of Sciences, Beijing, China 100068;

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会议组织
原文格式 PDF
正文语种 eng
中图分类环境遥感;
关键词
high-resolution remote sensing; vegetation; classification; knowledge pool; texture;

机译：高分辨率遥感;植被;分类;知识库;纹理;

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4. Vegetation Classification Model Based on High-resolution Satellite Imagery [C] . Chen Junying, Tian Qingjiu, SPIE-The International Society for Optical Engineering, National Symposium on Remote Sensing of China . 2006

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Vegetation Classification Model Based on High-resolution Satellite Imagery

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