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  • 1Ecophysiology and RS-GIS Laboratory, Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara, India.

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使用空中可见/红外成像光谱仪-下一代 (AVIRIS-NG) 和机器学习的高光谱成像精确地分类了植物功能类型 (PFT). 梯度增强机 (GBM) 模型在印度古吉拉特州的PFT区分方面取得了很高的准确性.

关键词:
一个功能性的功能性功能.学习学习学习学习学习学习机器机器机器机器机器机器这是光谱的光谱.

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 生态生态学 生态生态学
  • 计算机科学 计算机科学

背景情况:

  • 传统的土地覆盖分类方法缺乏细节来捕捉植物生理学和生物化学中的微妙变化.
  • 超光谱传感提供了详细的光谱特征,以改善森林分类和植物物种和植物功能类型 (PFT) 的差异化.

研究的目的:

  • 在印度古吉拉特邦Shoolpaneshwar野生动物保护区推进PFT的分类和监测.
  • 开发和使用一个全面的光谱库,使用高光谱数据和机器学习进行精确的PFT分类.

主要方法:

  • 使用下一代空中可见/红外成像光谱仪 (AVIRIS-NG) 和ASD手持光谱辐射仪 (400-1600nm) 获取超频谱数据.
  • 开发130种植物的光谱图书馆,并使用模糊C-means集群将它们分成五个PFT.
  • 使用ISODATA集群和Jeffries-Matusita (JM) 距离分析识别关键的光谱特征.
  • 机器学习分类器的评估:帕森窗口 (PW),梯度增强机器 (GBM) 和随机梯度下降 (SGD).

主要成果:

  • 梯度增强机器 (GBM) 分类器展示了最高的性能.
  • 在PFT分类中,GBM实现了0.94的整体准确度和0.93的卡帕系数.
  • 通过光谱分析实现了有效的特征选择,提高了分类准确性.

结论:

  • 超光谱传感与机器学习相结合,是准确的PFT分类和监控的关键工具.
  • 开发的光谱库和特征选择方法显著提高了PFT差异化的精度.
  • 该研究强调了遥感在生物多样性丰富的地区进行生态评估的潜力.