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用模拟训练深度学习对植被反射率进行大气校正,用于地面超频谱遥感

Farid Qamar^1,2,3, Gregory Dobler^4,5,6,7

¹Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, 19716, USA. qamar@udel.edu.

|July 29, 2023

概括

一种新的深度学习方法可以准确地从高光谱成像数据中提取植被光谱反射,即使没有大气信息. 这一进步使得使用地面传感器进行非破坏性生理研究成为可能.

科学领域:

遥感遥感遥感遥感
植物生理学植物生理学
机器学习机器学习

背景情况:

超光谱成像 (HSI) 提供了研究植被生理学的非侵入性方法.
从传感器数据中提取植被光谱反射率是具有挑战性的,因为大气干扰和传感器工件.
现有的方法往往需要大气数据,或者是为纳迪尔观测平台设计的,限制了它们用于地面侧向传感器的使用.

研究的目的:

从地面的HSI数据中开发一种准确的植被光谱反射率提取方法.
克服现有方法的局限性,特别是在缺乏大气参数知识和侧向传感器方向的情况下.
为了使植被生理状态的非破坏性评估.

主要方法:

一个依赖时间的编码解码器卷积神经网络被开发出来.
该网络经过训练和测试,使用模拟的光谱辐射数据,这些数据来自辐射转移建模.
模拟数据包括太阳角度,大气概况,植被反射率和传感器器件的变化.

主要成果:

拟议的框架在预测植被光谱反射率方面达到98.1% (±0.4) 的高测试准确度.
该方法表现出强大的性能,精度高于90%,即使在低光谱分辨率 (40频道,40 nm FWHM) 中也是如此.
与真实传感器数据的验证显示与复合比率方法一致,证实了其实际适用性.

结论:

一种新的方法可以从地面的HSI数据准确地估计植被光谱反射率.
这种方法即使没有精确的大气组成或条件数据,也有效.
这有助于准确的,非破坏性的植被监测与地面的HSI平台.

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