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基于高光谱激光雷达的多视角木叶三维重建研究

曹铮 邵慧 孙龙 胡玉霞 陈杰 徐恒 陈冲

量子电子学报2024,Vol.41Issue(4):659-670,12.
量子电子学报2024,Vol.41Issue(4):659-670,12.DOI:10.3969/j.issn.1007-5461.2024.04.010

基于高光谱激光雷达的多视角木叶三维重建研究

Research of multi-view wood-leaf 3D reconstruction based on hyperspectral lidar

曹铮 1邵慧 2孙龙 2胡玉霞 2陈杰 2徐恒 2陈冲2

作者信息

  • 1. 安徽建筑大学电子与信息工程学院,安徽 合肥 230601
  • 2. 安徽建筑大学电子与信息工程学院,安徽 合肥 230601||安徽省古建筑智能感知与高维建模国际联合研究中心,安徽 合肥 230601
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摘要

Abstract

Hyperspectral lidar(HSL)can obtain spatial and spectral information simultaneously,which provides more possibilities for three-dimention(3D)reconstruction of wood-leaf.The multi-view tree point clouds were collected using a self-developed HSL system for wood-leaf separation and 3D reconstruction.In order to meet the strict condition of traditional iterative closest point(ICP)algorithm,an improved point cloud registration method was proposed in this work.Firstly,the local feature of every point was described using fast point feature histogram(FPFH).Then,rough and accurate registration of point clouds were successively conducted based on random sample consensus(RANSAC)algorithm and ICP algorithm respectively.Finally,the wood and leaf components were separated based on the spectral data of the registered point clouds,the feature bands were extracted for wood and leaf separation on the basis of the spectral feature channel selection with random forest(RF)algorithm and support vector machine(SVM)algorithm,and 3D reconstruction of wood-leaf components was completed based on separation under different view.The experiment results show that the proposed improved point cloud registration method can achieve excellent registration accuracy both under 15° and 30° view angle difference,and the corresponding separation accuracy using RF algorithm based on feature channel selection reaches 98.17%and 98.87%,respectively.

关键词

遥感/高光谱激光雷达/空间-光谱信息/三维重建

Key words

remote sensing/hyperspectral lidar/spatial-spectral information/3D reconstruction

分类

信息技术与安全科学

引用本文复制引用

曹铮,邵慧,孙龙,胡玉霞,陈杰,徐恒,陈冲..基于高光谱激光雷达的多视角木叶三维重建研究[J].量子电子学报,2024,41(4):659-670,12.

基金项目

红外与低温等离子体安徽省重点实验室开放课题(IRKL2023KF04),安徽省住房城乡建设科学技术计划项目(2022-YF077),浙江省海洋大数据挖掘与应用重点实验室开放课题(OBDMA202103),安徽省高校省级自然科学研究项目(2023AH050181,KJ2021JD16) (IRKL2023KF04)

量子电子学报

OA北大核心CSTPCD

1007-5461

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