测绘通报 ›› 2024, Vol. 0 ›› Issue (11): 115-119.doi: 10.13474/j.cnki.11-2246.2024.1120

• 技术交流 • 上一篇    

基于颜色和影像指导的近岸海域三维点云分类和建模方法

余锐1,2,3, 张卉冉1,2,3, 廖剑波1,2,3   

  1. 1. 广州市城市规划勘测设计研究院有限公司, 广东 广州 510060;
    2. 广州市资源规划和海洋科技协同创新中心, 广东 广州 510060;
    3. 广东省城市感知与监测预警企业重点实验室, 广东 广州 510060
  • 收稿日期:2024-03-25 发布日期:2024-12-05
  • 作者简介:余锐(1986-),男,高级工程师,研究方向为大地测量、变形监测数据处理、海洋测绘。E-mail:375745905@qq.com
  • 基金资助:
    广州市资源规划和海洋科技协同创新中心项目(2023B04J0301;2023B04J0191);广东省重点领域研发计划(2020B0101130009);广东省城市感知与监测预警企业重点实验室基金(2020B121202019);广州市城市规划勘测设计研究院科技基金(RDI2220201029)

Classification and modeling of 3D point clouds in offshore waters based on color and image guidance

YU Rui1,2,3, ZHANG Huiran1,2,3, LIAO Jianbo1,2,3   

  1. 1. Guangzhou Urban Planning&Design Survey Research Institute, Guangzhou 510060, China;
    2. Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China;
    3. Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early arning, Guangzhou 510060, China
  • Received:2024-03-25 Published:2024-12-05

摘要: 针对从点云数据中进行近岸海域地物分类存在精度损失及在水面与滩涂划分界限模糊等问题,本文提出了一种基于颜色和影像指导的近岸海域三维点云分类和建模方法。该方法先以颜色分布直方图指导地物粗分类,然后采用SVM实现地物精分类,基于影像的二维分类结果指导三维点云数据分类,并在此基础上对海岛和滩涂等地物进行三维模型重建。试验结果表明,本文提出的分类和建模方法能够精确可靠地划分近岸海域地物,实现地物三维模型重建。

关键词: 点云分类, 三维模型重建, 海岸带, 海岛, 滩涂

Abstract: In order to solve the problems of accuracy loss and blurring the boundaries between sea surface and mudflat in coastal sea features classification, we proposes a 3D point cloud classification and modeling method in offshore waters through color and image-guidance strategies. This method first uses the color distribution histogram to guide the rough classification of ground objects, and then uses SVM to achieve fine classification of ground objects. The 2D classification results will then be optimally mapped to the 3D point cloud, and on this basis, 3D model reconstruction of islands, mudflats and other features is carried out. The experimental result indicated the proposed method can accurately and reliably classify offshore sea features and achieve 3D model reconstruction of surface features.

Key words: point cloud classification, 3D reconstruction, coastal zone, island, mudflat

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