A Novel Unsupervised Segmentation Method of Canopy Images from UAV Based on Hybrid Attention Mechanism

Abstract

Crown segmentation is a pivotal process in the acquisition of tree parameters. In light of the high expenses associated with satellite remote sensing and LiDAR technologies, our study leverages the cost-effective and efficient UAV remote sensing technology for capturing crown images. In addition, considering the expense and sensitivity associated with labeling data for supervised learning and its implications on model generalization and label quality, this paper introduces an innovative unsupervised learning framework based on convolutional neural networks (CNN). To address the limited receptive field of CNN, we have introduced a novel hybrid attention module following each CNN module. This enhancement ensures the integrity of the segmentation results and the coherence of the boundaries. Furthermore, in response to the growing need for user interaction, we have incorporated a scribble interaction function. Through the semantic segmentation of the collected crown images, our proposed method attains remarkable results, achieving an accuracy of 98.15%, an F1_score of 97.01%, and an mIoU of 95.58%. Additionally, we have conducted a comparative analysis of our proposed method with two clustering algorithms, namely K-Means and GMM, and two CNN models, DeepLab and U-Net. The results reveal that our segmentation structures outperform other methods significantly. The experimental findings demonstrate the immense application potential of this method in diverse fields, including forestry management, environmental protection, and ecosystem monitoring.