树分网 (TreeSeg-Net):一个端到端的实例分段网络,用于树叶脱落的森林点云,使用全球背景和空间近距离
Xingmei Xu1, Ruihang Zhang1, Shunfu Xiao2
1College of Information Technology, Jilin Agricultural University, Changchun 130118, China.
Plants (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了TreeSeg-Net,这是一个先进的深度学习模型,用于精确地从复杂的森林点云中细分单个树木. 它提高了森林库存的准确性,特别是在脱叶季节.
科学领域:
- 林业科学 林业科学
- 计算机视觉 计算机视觉 计算机视觉
- 遥感是一种远程传感.
背景情况:
- 森林生态系统对于碳循环和生物多样性至关重要.
- 无人驾驶飞行器 (UAV) 技术提供了具有成本效益的森林数据.
- 复杂森林中的脱落状况,由于交织的树冠和模糊的边界,造成了细分挑战.
研究的目的:
- 开发一种自动化方法,从无人机衍生的点云中精确地分类单个树.
- 为了解决复杂的现有细分技术的局限性,叶子脱落的森林环境.
- 通过精确的树参数提取来改善森林资源管理.
主要方法:
- 提出了TreeSeg-Net,这是一个用于原始点云的端到端实例细分网络.
- 整合了一个全球上下文注意模块 (GCAM) 来捕捉远程依赖.
- 引入了一个具有几何约束的空间近距离权重模块 (SPWM),以减少低细分.
主要成果:
- 树木Seg-Net实现了97.2%的平均精度 (AP),例如分段.
- 该网络在语义细分方面实现了99.7%的平均交叉与联盟 (mIoU).
- 与主流细分网络相比,证明了更高的准确性.
结论:
- 树木Seg-Net提供了一个高效和自动化的解决方案,用于精确的森林资源库存.
- 该方法有效地处理复杂的森林结构和细分挑战.
- 能够直接从点云数据中准确地分离单个树.
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