MDC-Net: a multi-directional constrained and prior assisted neural network for wood and leaf separation from terrestrial laser scanning

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.

Keywords

• terrestrial laser scanning
• wood and leaf separation
• deep learning
• prior features