MDC-Net: a multi-directional constrained and prior assisted neural network for wood and leaf separation from terrestrial laser scanning. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Dai, Wenxia; Jiang, Yiheng; Zeng, Wen; Chen, Ruibo; Xu, Yongyang; Zhu, Ningning; Xiao, Wen; Dong, Zhen; Guan, Qingfeng
• Is Part Of: International journal of digital earth. Volume 16:Issue 1(2023)
• Page Start: 1224
• Page End: 1245
• Publication Date: 2023-12-31
• Subjects: Terrestrial laser scanning -- wood and leaf separation -- deep learning -- prior features; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2023.2198261
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT : Wood-leaf separation from terrestrial laser scanning (TLS) is a crucial prerequisite for quantifying many biophysical properties and understanding ecological functions. In this study, we propose a novel multi-directional collaborative convolutional neural network (MDC-Net) that takes the original 3D coordinates and useful features from prior knowledge (prior features) as input, and outputs the semantic labels of TLS point clouds. The MDC-Net contains two key units: (1) a multi-directional neighborhood construction (MDNC) unit to obtain more representative neighbors and enable directionally aware feature encoding in the subsequent local feature extraction, to mitigate occlusion effects; (2) a collaborative feature encoding (CFE) unit is introduced to incorporate useful features from prior knowledge into the network through a collaborative cross coding to enhance the discrimination for thin structures (e.g. small branches and leaf). The MDC-Net is evaluated on five plots from forests in Guangxi, China, with different branch architectures and leaf distributions. Experimental results showed that the MDC-Net achieved an OA of 0.973 and a mIoU of 0.821 and outperformed other related methods. We believe the MDC-Net would facilitate the usage of TLS in ecology studies for quantifying tree size and morphology and thus promote the development of relevant ecological applications.