高光谱遥感图像大气校正研究进展

doi:10.6046/zrzyyg.2021371

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摘要

大气校正是高光谱遥感图像预处理的重要步骤之一,大气校正的精度在一定程度上决定了高光谱遥感应用的程度。首先,分析了大气对辐射传输的影响,并对大气中气溶胶光学厚度和水汽的反演方法作了总结,说明了影响高光谱遥感图像质量的主要大气因素; 其次,通过阐明辐射传输方程的推导过程及相关参数的作用机理,从理论上对大气的影响进行了论证,说明了高光谱大气校正的主要内容; 然后,总结了近年来形成的高光谱大气校正方法,包括基于经验统计的方法和基于辐射传输的方法,并对高光谱大气校正的研究进展与发展趋势进行分析; 最后,对高光谱遥感图像大气校正的未来发展进行了展望,为高光谱遥感的工程应用与研究提供参考。

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	孔卓
	杨海涛
	郑逢杰
	李扬
	齐济
	朱沁雨
	杨忠霖

关键词 ：高光谱遥感, 大气校正, 辐射传输方程, 辐射传输模型, 人工神经网络

Abstract：

Atmospheric correction is an important preprocessing step for hyperspectral remote sensing images. The atmospheric correction quality determines the application degree of hyperspectral remote sensing to a certain extent. First, this study analyzed the influence of the atmosphere on radiative transfer and summarized the inversion methods of aerosol optical thickness and water vapor in the atmosphere, indicating the main atmospheric factors affecting the quality of hyperspectral remote sensing images. Then, the influence of the atmosphere was demonstrated theoretically by clarifying the derivation process of the radiative transfer equation and the action mechanism of relevant parameters, indicating the main aspects of hyperspectral atmospheric correction. Furthermore, this study summarized the hyperspectral atmospheric correction methods formed in recent years, including methods based on empirical statistics and radiative transfer, and analyzed the study advances and development trends of hyperspectral atmospheric correction. Finally, this study forecasted the development of atmospheric correction of hyperspectral remote sensing images. This study will provide a certain reference for the engineering application and study of hyperspectral remote sensing.

Key words： hyperspectral remote sensing atmospheric correction radiative transfer equation radiative transfer model artificial neural network

收稿日期: 2021-11-05 出版日期: 2022-12-27

ZTFLH:

TP79

基金资助:基础加强计划重点基础研究项目(2020-JCJQ-ZD-152-12-04)

通讯作者: 杨海涛(1979-),男,博士,副教授,主要从事航天遥感等方面的研究。Email: yanghtt@126.com。

作者简介: 孔卓(1998-),男,硕士研究生,主要从事高光谱遥感方面的研究。Email: kz951525758@163.com。

引用本文:

孔卓, 杨海涛, 郑逢杰, 李扬, 齐济, 朱沁雨, 杨忠霖. 高光谱遥感图像大气校正研究进展[J]. 自然资源遥感, 2022, 34(4): 1-10.
KONG Zhuo, YANG Haitao, ZHENG Fengjie, LI Yang, QI Ji, ZHU Qinyu, YANG Zhonglin. Research advances in atmospheric correction of hyperspectral remote sensing images. Remote Sensing for Natural Resources, 2022, 34(4): 1-10.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021371 或 https://www.gtzyyg.com/CN/Y2022/V34/I4/1

Tab.1 国内外部分星载高光谱成像仪技术指标

Fig.1 辐射传输过程示意图

Fig.2 不同大气分子成分对辐射传输的影响

模型	介绍
6S	6S是5S的改进版本,它将大气-地表系统耦合在大范围的大气、光谱和几何条件下,有效模拟了太阳辐射。采用连续散射阶算法,改善了散射效应的计算。并将大气进行分层,每层通过迭代辐射传输方程进行计算,可计算0.24~4.0 μm的地表反射率
MODTRAN	MODTRAN是对LOWTRAN的改进模型,它几乎考虑了大气中所有大气分子、气溶胶及云的吸收和散射效应,并对分子吸收算法进行了改进,将光谱分辨率提高到了1 nm,可以计算波数为0.2 μm至无穷大的大气透射率和辐射。6S与MODTRAN是构成其他大部分辐射传输模型的基本模型,这2种模型模拟了辐射传输的物理机制,精度较高,但模拟不可能完全准确,且不能实现对光谱的打磨等后处理
ATREM	ATREM是开发最早的高光谱大气辐射传输模型之一,它在Malkmus窄带模型推导大气透射光谱的基础上创建水汽查找表,并结合三波段比值法得到每个像元的水汽含量。采用6S代码模拟大气散射作用,最终结果是逐像元估计的水汽含量图及辐射校正图。目前已停止支持使用。ATREM由于没有考虑邻近效应的影响,因此在雾霾和高对比度杂波环境中不太准确
HATCH	HATCH是在ATREM基础上的一种改进模型,主要改进包括采用k-相关方法从高分辨率透射分子吸收数据库(HITRAN)推导大气透射,使用了一个快速辐射传输方程求解器,并改进了水汽反演的三波段比值法,同时,它采用了光谱重校准方法,使得计算精度得到了提高。但与ATREM一样,HATCH同样没有考虑邻近效应的影响
ACORN	ACORN移植了MODTRAN的代码,使用MODTRAN生成水汽查找表,然后根据高光谱数据进行最小二乘拟合。ACORN的一个关键改进是进行了全光谱拟合,以解决地表植被中水蒸气和液态水吸收重叠的问题。但ACORN在计算过程中同样没有考虑邻近效应
FLAASH	FLAASH也是在MODTRAN基础上开发的辐射传输模型,它利用图像数据与大气特性,以物理方法精确推算地表反射率,并在计算过程中考虑了邻近效应的影响,同时能够校正倾斜观测导致的误差,计算精度得到了很大提升,目前已集成于ENVI软件中,已在高光谱遥感中得到了广泛的应用。但FLAASH计算结果中可能会出现负反射率,且不能进行有效改善
ATCOR	ATCOR使用了MODTRAN的辐射传输代码和HITRAN数据库,能够很好地处理邻近效应,ATCOR3及之后版本可支持高光谱图像运算,在机载高光谱的大气校正中应用较为广泛,而在卫星遥感中适用于处理中小视场卫星图像

Tab.2 常见辐射传输模型

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