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

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International Journal of Digital Earth 2023年第1期16卷 1224-1245页

作者： Wenxia Dai Yiheng Jiang Wen Zeng Ruibo Chen Yongyang Xu Ningning Zhu Wen Xiao Zhen Dong Qingfeng Guan School of Geography and Information Engineering China University of GeosciencesWuhanPeople’s Republic of China Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing NanningPeople’s Republic of China School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China National Engineering Research Center of Geographic Information System China University of GeosciencesWuhanPeople’s Republic of China

Wood-leaf separation from terrestrial laser scanning(TLS)is a crucial prerequisite for quantifying many biophysical properties and understanding ecological *** 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 *** 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(*** branches and leaf).The MDC-Net is evaluated onfive plots from forests in Guangxi,China,with different branch architectures and leaf *** results showed that the MDC-Net achieved an OA of 0.973 and a mIoU of 0.821 and outperformed other related *** 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.

关键词： Terrestrial laser scanning wood and leaf separation deep learning prior features

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