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机器学习方法用于使用光谱分析对森林物种进行分类.

Paurava G Thakore1, Grant W Erbelding1, Joydeep Bhattacharjee2

  • 1Plant Ecology Lab, School of Sciences, University of Louisiana Monroe, 700 University Avenue, Monroe, LA, 71209, USA.

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概括

消费级无人机系统 (UAS) 和机器学习准确地对底部硬木森林中的树种进行分类. 卷积神经网络 (CNN) 的性能优于其他方法,有助于森林管理和生态监测.

关键词:
底部地区的硬木森林是底部地区的硬木森林.基于对象的图像分析.随机的森林 随机的森林遥感是一种远程传感.在U-net中,U-net是指U-net网络.无人驾驶飞行系统 无人驾驶飞行系统

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科学领域:

  • 生态生态学 生态生态学
  • 遥感 遥感 遥感 遥感
  • 林业林业 林业 林业 林业

背景情况:

  • 生态系统监测和物种分类通过高分辨率遥感来增强.
  • 无人机系统 (UAS) 为生态研究提供了具有成本效益的方法.

研究的目的:

  • 评估无人机系统 (UAS) 和机器学习,以对底部硬木森林 (BHF) 中的主导树种进行分类.
  • 为了比较卷积神经网络 (CNN) 和基于对象的图像分析 (OBIA) 方法在树种分类方面的有效性.

主要方法:

  • 使用UAS收集了高分辨率RGB空中图像.
  • 使用光度计技术处理图像,以创建orthomosaics和纹理特征.
  • 通过基于对象的图像分析 (OBIA) 分段应用了U-Net卷积神经网络 (CNN) 和随机森林分类器.

主要成果:

  • 对于九种主要的树种,CNN方法的分类准确度高于OBIA.
  • 使用CNN方法,Quercus物种表现出最高精度的83.3%.
  • 该研究确定了UAS和机器学习在森林物种库存和生态监测中的潜力.

结论:

  • 无人机图像与机器学习相结合,特别是CNN,显示出对准确的树种分类有很大的希望.
  • 通过整合多时间数据和先进传感器的进一步研究,可以提高分类准确性和环境监测中的实际应用.
  • 这些发现支持无人机遥感在生态研究和森林保护中的日益有用性.