Using UAV LiDAR Intensity Frequency and Hyperspectral Features to Improve the Accuracy of Urban Tree Species Classification

Abstract

Accurately classifying tree species in urban forests remains a major challenge due to the limitations of existing methods. While hyperspectral imaging is capable of capturing detailed spectral information, it struggles with the issue of ‘similar reflectance curves for different species’. On the other hand, the intensity frequency curve also has the problem of ‘similar intensity frequency curves for different species’. To overcome these challenges, a new method is proposed in this study. The method combines UAV LiDAR intensity frequency features (IF) and hyperspectral features (HF) to accurately identify tree species, abbreviated as HICM. The results of the study demonstrate that HICM can accurately identify 16 common urban tree species with an overall accuracy (OA) of 92.0% and kappa of 91.2%. Compared with using only IF or HF for identification, HICM improves OA by 16.9% and 12%, and kappa by 21.6% and 10.1%, respectively. This shows that HF and IF have complementarity, and the combination of these two features effectively addresses the problems of “same species different frequency” and “same species different spectrum”.

Keywords

• Hyperspectral
• light detection and ranging (LiDAR)
• mask R-convolutional neural networks (CNN)
• tree species classification