基于高光谱激光雷达的多视角木叶三维重建研究

doi:10.3969/j.issn.1007-5461.2024.04.010

量子电子学报 ›› 2024, Vol. 41 ›› Issue (4): 659-670.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

曹铮1, 邵慧1,2*, 孙龙1,2, 胡玉霞1,2, 陈杰1,2, 徐恒1,2, 陈冲1,2

( 1 安徽建筑大学电子与信息工程学院, 安徽合肥 230601; 2 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 )

收稿日期:2022-05-20 修回日期:2022-05-26 出版日期:2024-07-28 发布日期:2024-07-28
通讯作者: E-mail: shaohui@ahjzu.edu.cn E-mail:E-mail: shaohui@ahjzu.edu.cn
作者简介:曹铮 ( 1996 - ), 安徽安庆人, 研究生, 主要从事雷达信号处理方面的研究。E-mail: 2455373203@qq.com
基金资助:
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)

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

CAO Zheng1 , SHAO Hui 1,2*, SUN Long1,2 , HU Yuxia1,2 , CHEN Jie1,2 , XU Heng1,2 , CHEN Chong1,2

( 1 School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China; 2 Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Hefei 230601, China )

Received:2022-05-20 Revised:2022-05-26 Published:2024-07-28 Online:2024-07-28

摘要/Abstract

摘要： : 高光谱激光雷达 (HSL) 能实现空间-光谱信息的同步采集, 为树木的木叶三维重建提供了可能。利用自研的 HSL系统获取不同视角的树木点云数据, 进行了多视角下的木叶分离，并根据分离结果完成了的木叶三维重建。针对传统最近邻迭代 (ICP) 算法严格的点云初始条件需求, 提出了改进的点云配准方法。首先, 采用快速点特征直方图 (FPFH) 描述点云的局部特征, 然后基于随机抽样一致性 (RANSAC) 算法实现不同视角点云的粗配准, 最后利用 ICP算法实现精配准。在选出特征通道的基础上, 利用随机森林 (RF) 和支持向量机 (SVM) 完成木叶分离, 最终完成木叶三维重建。实验表明, 在15°和30°的视角差下, 提出的改进点云配准方法均达到较好的配准精度, 同时基于特征通道选择的RF方法木叶分离准确度分别达到98.17%和98.87%。

关键词: 遥感, 高光谱激光雷达, 空间-光谱信息, 三维重建

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.

Key words: remote sensing, hyperspectral lidar, spatial-spectral information, 3D reconstruction

中图分类号:

TP391

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.

CAO Zheng , SHAO Hui , SUN Long, , HU Yuxia, , CHEN Jie, , XU Heng, , CHEN Chong, . Research of multi‑view wood‑leaf 3D reconstruction based on hyperspectral lidar[J]. Chinese Journal of Quantum Electronics, 2024, 41(4): 659-670.

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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

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

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