南昌工程学院学报2024，Vol.43Issue(4)：56-62,7.
无人机遥感反演小麦地上生物量模型的特征选择
Feature selection of wheat field biomass model retrieved by UAV remote sensing
摘要
Abstract
The unmanned aerial vehicle(UAV)multispectral technique is a popular method for rapid and nondestructive de-termination for field biomass(AGB)of wheat.However,the multispectral method usually produces a large number of highly correlated repetitive features in the calculation of vegetation features,so it is of great significance to features selection and determine the model with simple structure and high precision.In this paper,a hybrid coded Grey Wolf particle swarm opti-mization(CGWOPSO)algorithm was proposed,which can achieve both feature screening and parameter optimization.To e-valuate the performance of this method,the performance of two popular feature selection methods(Pearson and SHAP meth-ods)driven by Extreme gradient boosting model(XGBoost)for AGB was compared.The results show that the AGB model based on the SHAP method yield RMSE 3.0％to 16.3％lower than the Pearson method.The accuracy of CGWOPSO-XGB model was higher than that of XGB model based on SHAP method,and its RMSE is 16.0％lower than that of the latter.关键词
混合编码/灰狼粒子群优化算法/SHAP/特征筛选/植被指数Key words
hybrid coding/Grey Wolf particle swarm optimization algorithm/SHAP/feature selection/vegetation index分类
农业科技引用本文复制引用
吴立峰,徐文浩,韩宜秀..无人机遥感反演小麦地上生物量模型的特征选择[J].南昌工程学院学报,2024,43(4):56-62,7.基金项目
江西省教育厅科学技术研究项目(GJJ211904) （GJJ211904）
江西省科技厅重点研发项目(20212BDH80016) （20212BDH80016）
