Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

YANG Xiao-Hua; WANG Fu-Min; HUANG Jing-Feng; WANG Jian-Wen; WANG Ren-Chao; SHEN Zhang-Quan; WANG Xiu-Zhen

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Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

机译：径向基函数神经网络与回归模型估算水稻生物物理参数的比较

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The radial basis function (RBF) emerged as a variant of artificial neural network.Generalized regression neural network (GRNN) is one type of RBF,and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets.Hyperspectral reflectance (350 to 2 500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars,three nitrogen treatments and one plant density (45 plants m-2).Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reflectance (R) and its three different transformations,the first derivative reflectance (D1),the second derivative reflectance (D2) and the log-transformed reflectance (LOG).GRNN based on D1 was the best model for the prediction of rice LAI and GLCD.The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed.Owing to its strong capacity for nonlinear mapping and good robustness,GRNN could maximize the sensitivity to chlorophyll content using D1.It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.

机译：径向基函数（RBF）成为人工神经网络的一种变体。广义回归神经网络（GRNN）是RBF的一种，其主要优点是可以快速学习并快速收敛到大量的最优回归曲面在具有两个品种，三个氮处理和一个植物密度（45株m-2）的两个实验田的两个不同水稻站点上记录了高光谱反射率（350至2500 nm）数据。逐步多元回归模型（SMR））和RBF用于根据反射率（R）及其三种不同的转换（一阶导数反射率（D1），二阶导数）比较它们对水稻的叶面积指数（LAI）和绿叶叶绿素密度（GLCD）的可预测性反射率（D2）和对数转换反射率（LOG）。基于D1的GRNN是预测水稻LAI和GLCD的最佳模型。反射率和ric的不同转换之间的关系当使用RBF时，可以进一步改善参数。由于其强大的非线性映射能力和良好的鲁棒性，GRNN可以使用D1最大化对叶绿素含量的敏感性。结论是RBF可以为估算提供有用的探索性和预测性工具水稻生物物理参数。

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来源
《土壤圈（英文版）》 |2009年第2期|176-188|共13页
作者
YANG Xiao-Hua; WANG Fu-Min; HUANG Jing-Feng; WANG Jian-Wen; WANG Ren-Chao; SHEN Zhang-Quan; WANG Xiu-Zhen;
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作者单位

Institute of Agricultural Remote Sensing & Information Application,Zhejiang University,Hangzhou 310029 China;

Meteorological and Hydrographic Department of General Staff Headquarters,Beijing 100081 China;

Institute of Agricultural Remote Sensing & Information Application,Zhejiang University,Hangzhou 310029 China;

Institute of Agricultural Remote Sensing & Information Application,Zhejiang University,Hangzhou 310029 China;

Meteorological and Hydrographic Department of General Staff Headquarters,Beijing 100081 China;

Institute of Agricultural Remote Sensing & Information Application,Zhejiang University,Hangzhou 310029 China;

Institute of Agricultural Remote Sensing & Information Application,Zhejiang University,Hangzhou 310029 China;

Zhejiang Meteorological Institute,Hangzhou 310004 China;

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收录信息中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
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中图分类农业基础科学;
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Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

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