Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

SHI Yong-jun; XU Xiao-jun; DU Hua-qiang; ZHOU Guo-mo; JIN Wei; ZHOU Yu-feng

Volume 25 Issue 4

Aug. 2008

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Article Navigation > Journal of Zhejiang A&F University > 2008 > 25(4): 417-421

SHI Yong-jun, XU Xiao-jun, DU Hua-qiang, ZHOU Guo-mo, JIN Wei, ZHOU Yu-feng. Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network[J]. Journal of Zhejiang A&F University, 2008, 25(4): 417-421.

Citation:

SHI Yong-jun, XU Xiao-jun, DU Hua-qiang, ZHOU Guo-mo, JIN Wei, ZHOU Yu-feng. Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network[J]. Journal of Zhejiang A&F University, 2008, 25(4): 417-421.

Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

1.
School of Environmental Sciences and Technology，Zhejiang Forestry College，Lin’an 311300，Zhejiang，China

More Information

Corresponding author: ZHOU Guo-mo
Publish Date: 2008-08-20

Abstract

To estimate the carbon content of bamboo forest based on remote sensing，highly accurate data acquisition is necessary to reduce estimation errors. In this study，enhanced thematic mapper plus（ETM+） remote sensing data was used to extract bamboo forest data using a back propagation（BP） neural network. Matlab program language（Version 7.1） was used to compile the classification algorithm with algorithms of three training functions being compared；namely，Traingda-gradient descent backpropagation with adaptive learning rate backpropagation；Trainlm-levenberg-marquardt backpropagation；and Traingdm-gradient descent with momentum backpropagation. Results showed that for bamboo forest the BP neural network had a high classification accuracy with a producer accuracy of 84.0% and a user accuracy of 98.7%. Meanwhile，of the three different training functions Traingda had the highest classification accuracy，whereas Trainlm had the shortest training time. ［Ch，3 fig. 3 tab. 17 ref.］
- forest management,
- back propagation（BP） neural network,
- bamboo forest,
- classification,
- remote sensing,
- enhanced thematic mapper plus （ETM+）

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

1. School of Environmental Sciences and Technology，Zhejiang Forestry College，Lin’an 311300，Zhejiang，China

Corresponding author: ZHOU Guo-mo

Publish Date: 2008-08-20

Key words:

Abstract: To estimate the carbon content of bamboo forest based on remote sensing，highly accurate data acquisition is necessary to reduce estimation errors. In this study，enhanced thematic mapper plus（ETM+） remote sensing data was used to extract bamboo forest data using a back propagation（BP） neural network. Matlab program language（Version 7.1） was used to compile the classification algorithm with algorithms of three training functions being compared；namely，Traingda-gradient descent backpropagation with adaptive learning rate backpropagation；Trainlm-levenberg-marquardt backpropagation；and Traingdm-gradient descent with momentum backpropagation. Results showed that for bamboo forest the BP neural network had a high classification accuracy with a producer accuracy of 84.0% and a user accuracy of 98.7%. Meanwhile，of the three different training functions Traingda had the highest classification accuracy，whereas Trainlm had the shortest training time. ［Ch，3 fig. 3 tab. 17 ref.］

Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Related

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