增强的DeepLabV3+与OBIA和轻量级注意力,用于在无人机图像中准确有效地分类树种
Xue Cheng1, Jianjun Chen1,2, Junji Li1
1College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了改进的DeepLabV3+模型,用于精确地从无人机图像中分类树种. 改进后的模型实现了更高的准确性,并大大降低了森林管理的计算负载.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 准确的树种分类对于森林研究和管理至关重要.
- 现有的方法面临类似物种,复杂的天窗和计算成本的挑战.
研究的目的:
- 利用无人机成像开发一种高精度,轻量级的树木物种分类模型.
- 通过整合OBIA和注意力机制来提高现有的DeepLabV3+性能.
主要方法:
- 基于对象的图像分析 (OBIA) 用于多尺度细分和边界优化.
- 用于特征选择的递归特征消除与交叉验证 (RFECV).
- 随机森林分类与视觉解释 (RFVI) 用于标签生成.
- 一个轻量级的注意力模块集成到DeepLabV3+.
主要成果:
- 改进的DeepLabV3+模型实现了94.91%的整体准确率和92.89%的卡帕系数.
- 模型参数减少了78.35% (至5.91M).
- 与原来的DeepLabV3 +,U-Net和PSPNet相比,显著提高了性能.
结论:
- 拟议的方法提供了一个高精度,计算效率高的解决方案,用于自动化树木物种映射.
- 这项技术支持森林碳监测和生态管理.
- 整合OBIA和注意力机制增强了基于无人机的森林分析.
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