Study on Classification of Arbor Tree Species at Single Tree Scale based on Cross-modal Hybrid Fusion of UAV Point Cloud and Image
To explore the application potential of airborne point cloud and UAV visible light image in tree spe-cies identification and classification,a single-tree scale tree species classification and recognition method based on UAV hybrid fusion of multi-modal features and decision was proposed.Firstly,Kendall Rank correlation co-efficient method and Permutation Importance(PI)were used for feature selection,and Efficient Low-Rank Multi-Mode Fusion Algorithm(LMF)was used to fuse the selected point cloud and visible image features.En-semble learning was introduced to input point cloud,image,and fusion features into eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Random Forest(RF)base classifiers inte-grated by Stacking.Finally,the meta classifier,Naive Bayes,is used for decision fusion.The experimental da-ta show that the independent test accuracy of the proposed algorithm is 99.4％,which improves 22.58％com-pared with the Random Forest classifier by traditional feature concatenate fusion.In addition,the Kappa coeffi-cient also increased by 28.54％.The comparison experiment with Convolutional Neural Network(CNN)shows that the proposed algorithm has obvious advantages in small sample training and better generalization ability.
Multimodal fusionSingle tree scaleTree species classificationEnsemble learning
NETL
NSTL
万方数据
维普
