Abundance Extraction of End-Members of Forest Based on Linear Mixed Model - A Case Study of Meijiang Basin in China

机译：基于线性混合模型的林木末梢丰度提取-以梅江盆地为例

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Because of ignoring mixed pixels, classification errors will be consequentially generated based on generally supervised classification by per pixel. This paper takes Meijiang River Basin as the investigative object. After MNF and PPI, main forest end-members (broadleaf forest end-member, coniferous forest end-member and low herbage end-member) abundances maps were obtained with Linear Spectral Model, combining with reality and a special device which helped us interactive selection. Results showed that the soft classification is an effective method of improving the precision of remote sensing classification to a certain extent.

机译：由于忽略了混合像素，因此将基于每个像素的一般监督分类结果而产生分类错误。本文以梅江流域为研究对象。在MNF和PPI之后，利用线性光谱模型，结合实际情况和帮助我们进行交互选择的特殊设备，获得了主要森林最终成员（阔叶森林最终成员，针叶林最终成员和低牧草最终成员）的丰度图。。结果表明，软分类是在一定程度上提高遥感分类精度的有效方法。

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来源
《Remote Sensing, Environment and Transportation Engineering (RSETE), 2012 2nd International Conference on》|2012年|p.1- 4|共4页
会议地点 Nanjing(CN)
作者
Chen Xuzhi; Lai Geying;
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正文语种 eng
中图分类环境遥感;
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Abundance Extraction of End-Members of Forest Based on Linear Mixed Model - A Case Study of Meijiang Basin in China

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