ACE R‐CNN: An Attention Complementary and Edge Detection‐Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

Abstract

Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine‐scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been con-ducted on the delineation of individual tree crown and species classification using drone high‐res-olution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environ-ments. In this study, we improve upon the state of the Mask region‐based convolution neural network (Mask R‐CNN) with our proposed attention complementary network (ACNet) and edge detection R‐CNN (ACE R‐CNN) for individual tree species identification in high‐density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R‐CNN. We demonstrate the performance of ACE R‐CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1‐score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50‐FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accel-erates the convergence speed of the model training. This work demonstrates the improved performance of ACE R‐CNN for individual tree species identification and provides a new solution for tree‐level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment.