首页> 外文期刊>Pedosphere: A Quarterly Journal of Soil Science >Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing
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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 2500 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 DI 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)作为人工神经网络的变体出现。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具有大量数据集的最优回归表面。在具有两个品种,三个氮处理和一个植物密度(45株m(-2))的两个实验田中的两个不同水稻站点记录了高光谱反射率数据(350至2500 nm)。使用逐步多变量回归模型(SMR)和RBF基于反射率(R)及其三种不同的转换(一阶导数反射率(R))比较稻米的叶面积指数(LAI)和绿叶叶绿素密度(GLCD)的可预测性D1),二阶导数反射率(D2)和对数变换反射率(LOG)。基于DI的GRNN是预测水稻LAI和GLCD的最佳模型。使用RBF可以进一步改善反射率的不同转换与水稻参数之间的关系。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可能为估算水稻生物物理参数提供有用的探索性和预测性工具。

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