叠加组合与机器学习回归器对最佳特征 (SMOF) 的超光谱传感器PRISMA用于内陆水度预测
Rajarshi Bhattacharjee1, Shishir Gaur2, Shard Chander3
1Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India. rajbhatt78645@gmail.com.
Environmental science and pollution research international
|November 24, 2024
概括
这项研究通过机器学习确定了用于内陆水域度预测的关键超谱频段. 一个新的框架,SMOF,实现了1.21%的MAPE,突出了第77波段的关键.
科学领域:
- 遥感 遥感 遥感 遥感
- 环境监测 环境监测
- 机器学习应用 机器学习应用
背景情况:
- 超光谱数据提供了丰富的信息,但管理众多的光谱频段是具有挑战性的.
- 准确的内陆水域度预测对于环境管理和水质评估至关重要.
- 确定最有信息的光谱频段对于高效的高光谱数据分析至关重要.
研究的目的:
- 确定用于预测内陆水域度的最相关的超谱频段.
- 开发和评估一种新的堆叠组合框架 (SMOF),用于增强度预测.
- 引入一种用于检测高频谱数据立方体中的噪声带的方法.
主要方法:
- 使用九个机器学习回归器 (例如,随机森林,XGBoost) 来计算超频谱频段的特征重要性.
- 开发了SMOF框架,使用随机森林作为基础和元模型.
- 实现了一个使用Renyi的来检测噪声带的统计算法,并使用水色模拟器 (WASI) 来进行光谱模拟.
主要成果:
- 随机森林实现了度预测的平均绝对百分比误差 (MAPE) 为1.61%和R2为0.96.
- 波段77 (1067.61 nm) 被确定为特征重要性最主要的波段.
- SMOF框架提高了预测准确度,达到1.21%的MAPE和0.95.95%的R2.
- 在PRISMA数据中确定了放射测量校准误差和水蒸气吸收作为主要噪声源.
结论:
- 该研究成功地确定了关键的超光谱波段,并开发了一种有效的集体模型来预测内陆水度.
- 拟议的噪声带检测算法有助于改进应用程序的超频谱数据质量.
- 这些发现为利用超光谱数据在水质监测中提供了一种有价值的方法.
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