集成功能增强和主动学习策略的GBDT方法 - - 波福特海海冰厚度逆转
Yanling Han1, Junjie Huang1, Zhenling Ma1
1Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
本研究引入了改进的渐变增强决策树 (GBDT) 模型,使用遥感数据准确逆转海冰厚度. 改进后的模型显著提高了准确性,解决了海冰监测中样本不足和数据噪声的局限性.
科学领域:
- 地球科学 地球科学 地球科学
- 遥感 遥感 遥感 遥感
- 气候科学 气候科学
背景情况:
- 海冰厚度对全球气候和人类活动至关重要.
- 目前用于海冰厚度逆转的遥感方法遭受样本不足和噪音干扰.
- 高空间分辨率数据往往含有有限的信息,影响反转精度.
研究的目的:
- 开发一种改进的渐变增强决策树 (GBDT) 方法,用于准确的海冰厚度逆转.
- 为了解决远程传感数据中样本不足和数据噪声的局限性.
- 为了提高海冰厚度反转模型的准确性和概括能力.
主要方法:
- 设计了一种改进的GBDT方法,集成功能增强和主动学习 (IFEAL-GBDT).
- 使用事件角度和时间序列数据应用时空校正.
- 生成了多属性特征,包括月份和海水温度,并采用了最大标准偏差的积极学习.
主要成果:
- 该IFEAL-GBDT方法实现了海冰厚度的高反转精度.
- 证明了平均绝对误差为8厘米,根平均平方误差为13.7厘米.
- 获得0.912的相关系数,优于其他方法.
结论:
- 拟议的IFEAL-GBDT方法有效地提高了海冰厚度逆转的准确性.
- 该方法适用于使用Sentinel-1数据的高精度海冰厚度逆转.
- 功能增强和积极学习策略显著提高模型性能.
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