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Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

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成果类型:
期刊论文
作者:
Xi, Yanbiao;Ren, Chunying*;Wang, Zongming;Wei, Shiqing;Bai, Jialing;...
通讯作者:
Ren, Chunying
作者机构:
[Xi, Yanbiao; Ren, Chunying; Wang, Zongming; Zhang, Bai; Xiang, Hengxing; Chen, Lin] Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Jilin, Peoples R China
[Wei, Shiqing] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China
[Bai, Jialing] Univ South China, Sch Nucl Resource Engn, Hengyang 421001, Peoples R China
通讯机构:
[Ren, Chunying] C
Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Jilin, Peoples R China.
语种:
英文
关键词:
deep learning;convolutional neural network;tree species classification;random forest;OHS-1 hyperspectral image
期刊:
Forests
ISSN:
1999-4907
年:
2019
卷:
10
期:
9
页码:
ARTN 818
基金类别:
thank the National Earth System Science Data Center for providing geographic information data.This study is supported by the National Key Research and Development projected of China (No. 2016YFC0500300), the Jilin Scientific and Technological Development Program (No. 20170301001NY) and Strategic Planning Project from Institute of Northeast Geography and Agroecology (IGA), Chinese Academy of Sciences (Y6H2091001).
机构署名:
本校为其他机构
院系归属:
核资源工程学院
摘要:
The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of...

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