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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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TM-WSNet:基于无人机LiDAR点云的个人树的精确细分方法.

Lele Yan1, Guoxiong Zhou1, Miying Yan1

  • 1Central South University of Forestry and Technology, Changsha, 410004, China.

Plant phenomics (Washington, D.C.)
|December 19, 2025
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概括

我们开发了TM-WSNet,这是一个新的细分网络,用于在种植园中精确识别树. 这种方法准确地估计了树的参数,有助于产量预测和健康监测.

关键词:
混合功能提取模块的混合功能提取模块.多层次功能融合多层次功能融合树的细分 树的细分规模优化算法 规模优化算法波形网格采样 波形网格采样

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科学领域:

  • 农业工程 农业工程
  • 计算机视觉 计算机视觉
  • 遥感 遥感 遥感 遥感

背景情况:

  • 单个树的细分对于种植园管理至关重要,但由于树冠干扰和树木变异性而具有挑战性.
  • 现有的方法难以准确地区分树木边界和处理各种树木形态.

研究的目的:

  • 开发一个高精度细分网络 (TM-WSNet),用于在复杂的种植环境中准确地细分单个树.
  • 改进用于精准农业应用的关键树参数的估计.

主要方法:

  • 拟议的TM-WSNet,采用集成变压器和Mamba架构的混合特征提取模块 (SGTramba).
  • 引入了一个波形网格特征融合编码器 (WGMS),用于增强结构特征识别和多尺度融合.
  • 实现了一个规模优化算法 (SCPO),用于在不同分辨率上调整自适应性学习速度.

主要成果:

  • 在多个数据集 (RubberTree,ShapeNetPart,ForestSemantic) 上,TM-WSNet实现了高分段精度和稳定性.
  • 实地测试显示,树高度 (R2=1.00),冠状宽度 (R2=0.99) 和胸高直径 (R2=0.89) 的准确预测.

结论:

  • TM-WSNet 在个人树细分技术方面取得了重大进展.
  • 该网络显示出在复杂农业环境中支持精确产量估计和健康监测的巨大潜力.