采用RNCA-PSO-ELM的水稻叶绿素光谱特征分析与反演  

Analysis and inversion of rice chlorophyll spectral characteristics using RNCA-PSO-ELM

作　　者：江凯伦 安吉庆 赵雨薇 罗俊盈 曹英丽[1,2] Jiang Kailun;An Jiqing;Zhao Yuwei;Luo Junying;Cao Yingli(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China;Liaoning Engineering,Research Center for Information Technology in Agriculture,Shenyang 110866,China)

机构地区：[1]沈阳农业大学信息与电气工程学院,沈阳110866 [2]辽宁省农业信息化工程技术中心,沈阳110866

出　　处：《农业工程学报》2022年第8期178-186,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：辽宁省重点研发计划项目(2020JH2/10200038);国家重点研发计划项目(2017YFD0300700)。

摘　　要：为探索有效的水稻叶绿素光谱特征选择方法与含量反演建模,解决东北粳稻叶绿素含量无人机遥感监测等问题,该研究利用沈阳农业大学卡力玛水稻实验站2018—2020年无人机(UnmannedAerialVehicle,UAV)水稻冠层高光谱数据及地面样本数据,设计了基于正则近邻成分分析的光谱特征选择方法,优化了其损失函数与正则化参数,获得水稻叶绿素不同含量的特征波段,并以此为输入,构建粒子群优化极限学习机叶绿素含量反演模型。结果表明:正则近邻成分分析算法具有较好的特征选择能力,其损失函数为均方误差损失函数、正则化参数值为0.306时,特征选择效果最佳,初选出权重非零的16个特征波段;进一步以叶绿素极限学习机反演精度为判据,优选出权重最高的6个特征波段:710、716、508、798、532和708 nm;应用粒子群优化算法优化了极限学习机模型的输入权值和阈值偏差,粒子群算法正交试验种群规模(POP)、惯性权重(IW)、学习因子(C1,C2)和速度位置相关系数(MC)的优选结果分别为50、1.5、1.3、3.5和0.6;基于正则近邻成分分析-粒子群优化极限学习机叶绿素含量反演结果的RMSE和R^(2)分别为9.549mg/L、0.891。研究结果可为基于无人机平台的高通量作物监测提供理论依据,并为筛选无人机高光谱波段实现作物长势参数快速估测提供参考。This study aims to explore the effective spectral feature selection and inversion modeling of rice chlorophyll content.Taking the Japonica rice as the research object, the Unmanned Aerial Vehicle(UAV) sensing was used to monitor the chlorophyll content at Shenyang Agricultural University Karima Rice Experiment Station in Northeast China in 2018-2020.The rice canopy hyperspectral data was also collected for the ground sample. A spectral feature selection was designed using the Regular Neighbor Component Analysis(RNCA), which was newly developed by the nearest NCA. A regularization term was then added to the loss function to reduce the risk of over-fitting. Different types of loss functions and regularization parameters were then tested to improve the feature extraction of RNCA. As such, the feature information was obtained from the hyperspectral to better predict the chlorophyll content of the rice plant. Subsequently, a chlorophyll inversion model was established using the input features. Extreme learning was also applied to build the model, due to its high accuracy and speed.Among them, the Extreme Learning Machine(ELM) was a feed-forward neural network with single or multiple hidden layers.Unlike the traditional back-propagation neural network, there was no need for reverse repetition, particularly after the parameters of the nodes were randomly set in the hidden layer of the ELM. Therefore, the amount of calculation was greatly reduced to learn and train the model faster than before. Furthermore, Particle Swarm Optimization(PSO) was selected to optimize the weights of the input layer and hidden layer deviations of ELM. The number of hidden layer nodes was automatically learned from the training data, instead of the randomness of the hidden layer node parameters of the ELM model.The generalization of the model was improved to reduce the number of hidden layer nodes for the target accuracy in practical applications. The results show that the RNCA presented the better feature selection. The best feature selection

关 键 词：无人机 高光谱 水稻 叶绿素含量 正则近邻成分分析 

分 类 号：S127[农业科学—农业基础科学] S511S252.2