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篇目详细内容 |
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【篇名】 |
Remote sensing monitoring of a bamboo forest based on BP neural network |
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【刊名】 |
Frontiers of Forestry in China(Ceased publication after completion of Volume 4 (2009)) |
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【刊名缩写】 |
Front. Forest. China |
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【ISSN】 |
1673-3517 |
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【EISSN】 |
1673-3630 |
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【DOI】 |
10.1007/s11461-009-0054-y |
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【出版社】 |
Higher Education Press and Springer-Verlag |
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【出版年】 |
2009 |
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【卷期】 |
4
卷3期 |
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【页码】 |
363-367
页,共
5
页 |
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【作者】 |
Yongjun SHI;
Xiaojun XU;
Huaqiang DU;
Guomo ZHOU;
Wei JIN;
Yufeng ZHOU;
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【关键词】 |
forest management; Back Propagation (BP) neural network; bamboo forest; classification; remote sensing; Enhanced Thematic Mapper+ (ETM+) |
【摘要】 |
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The collection of information on bamboo forests plays a crucial role in the calculation of carbon content reserves, and the acquisition of high-precision information will be good for reducing estimation errors. High precision is obtained with the adoption of a back propagation (BP) neural network to extract information on bamboo forests from Enhanced Thematic Mapper+ (ETM+) remote sensing images with the assistance of neural network modules provided by Matlab. We obtained a production precision of 84.04% and a user precision of 98.75%. We also conducted a comparison of classification differences of three training functions, i.e., the, Levenberg-Marquardt BP algorithm function (Trainlm), a gradient decreasing function of adaptive learning rate BP (Traingda), and a gradient lowering momentum BP algorithm function (Traingdm). Our analysis suggests that Traingda had the highest precision while Trainlm function required the shortest training time. |
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