A comparative study of the performances of joint RFE with machine learning algorithms for extracting Moso bamboo (Phyllostachys pubescens) forest based on UAV hyperspectral images

Abstract

Unmanned aerial vehicle (UAV) hyperspectral remote sensing technology has developed rapidly in recent years, providing a new scheme for intelligent monitoring of forest resources. This study aims to deeply mining the UAV hyperspectral information, and construct the best model for extracting Moso bamboo (Phyllostachys pubescens) forest information. Firstly, the spectral information features of Moso bamboo, broadleaf, bare area and shadow were obtained and their spectral anisotropies were analyzed. Secondly, multiple machine learning algorithms were combined with the recursive feature elimination (RFE) to obtain three sets of optimized feature subsets. Eventually, three sets of optimization features were substituted into multiple machine learning algorithms to build nine models of Moso bamboo information extraction, comparing their classification effects and generalization capabilities. The results showed that: (1) The subset of features optimized by different base classifiers in combination with RFE and the ranking results of optimized features’ importance were different, but all the three sets of optimized feature subsets showed relatively obvious feature difference patterns; (2) The nine models based on SVM-RFE, RF-RFE and XGBoost-RFE were all capable of extracting Moso bamboo information, among which, the classification effect of the XGBoost-RFE-XGBoost model was the best and had significant advantages, with the overall classification accuracy OA, Kappa coefficient and R 2 of 98.75%, 0.983 and 0.980, respectively, and the classification accuracy for Moso bamboo was 98.34%. Thus, XGBoost was the most applicable algorithm for feature optimization in combination with RFE, and the most effective algorithm with a better generalization ability for extracting Moso bamboo information from UAV hyperspectral images. This study is crucial for Moso bamboo’s large area monitoring and fine identification, providing a technical reference for obtaining information about the spatial and temporal distribution of Moso bamboo resources timely and accurately, facilitating the monitoring and management of them, and giving full play to their ecological and economic benefits.