Detection of Individual Trees in UAV LiDAR Point Clouds Using a Deep Learning Framework Based on Multichannel Representation

Abstract

Individual tree detection is critical for forest investigation and monitoring. Several existing methods have difficulties to detect trees in complex forest environments due to insufficiently mining descriptive features. This study proposes a deep learning (DL) framework based on a designed multichannel information complementarity representation for detecting trees in complex forest using UAV laser scanning point clouds. The proposed method consists of two main stages: ground filtering and tree detection. In the first stage, a modified graph convolution network with a local topological information layer is designed to separate the ground points. Unlike most existing parametric methods, our ground filtering method avoids the optimal parameters selection to adapt to different kinds of environments. For tree detection, a top-down slice (TDS) module is first designed to mine the vertical structure information in a top-down way. Then, a special multichannel representation (MCR) is developed to preserve different distribution patterns of points from complementary perspectives. Finally, a multibranch network (MBNet) is proposed for individual tree detection by fusing multichannel features, which can provide discriminative information for MBNet to detect trees more accurately. MBNet was evaluated on seven forest areas [UAV light detection and ranging (LiDAR) data with the mean size of 14 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$000~\text {m}^{2}$ </tex-math></inline-formula> and point density of 250 points/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {m}^{2}$ </tex-math></inline-formula> ]. Experimental results showed that the proposed framework achieves excellent performance. Our method obtains promising performance with a mean recall of 89.23% and a mean F1-score of 87.04%.