摘要
地表与大气的辐射传输耦合过程复杂,造成传统大气校正技术存在精度和效率的局限。基于经验线性方法的校正效率较高但精度有限,辐射传输模型方法虽精度较高但其高计算开销制约了多光谱卫星遥感的定量应用。随着神经网络的发展,物理模型与数据驱动方法的融合为大气校正提供新思路。文章提出一种深度神经网络框架,学习物理模型模拟数据集,在保证精度的同时显著提升处理效率。首先通过辐射传输模型在不同观测几何、大气状态和地表反射率特性下模拟卫星入瞳辐射数据,构建正向模拟多光谱遥感数据集。随后神经网络学习其中的地理几何和光谱特征,实现像素级的大气校正。实验表明,该方法相对于6S方法处理速度提升约六个数量级,平均绝对误差与相对误差分别为0.028和0.82;与主流物理模型工具在图像质量、定量指标及实测植被光谱对比中表现良好。该研究为多光谱遥感影像的定量应用提供了一种高效的大气校正路径。
The radiative transfer coupling between the Earth's surface and the atmosphere is highly complex,resulting in limitations in the accuracy and efficiency of traditional atmospheric correction techniques.Empirical linear methods offer relatively high computational efficiency but suffer from low precision,while radiative transfer model-based approaches achieve higher accuracy at the cost of substantial computation,restricting the quantitative application of multi-spectral satellite remote sensing.With the development of neural networks,integrating physical models with data-driven approaches has provided new vision for atmospheric correction.This study proposes a deep neural network framework trained on datasets simulated by radiative transfer models,which not only ensures correction accuracy but also significantly improves processing efficiency.First,radiative transfer model is used to simulate satellite top-of-atmosphere radiance under different observation geometries and atmospheric–surface conditions,thereby constructing a forward-simulated multi-spectral remote sensing dataset for data-driven atmospheric correction.The neural network then learns the geographic,geometric,and spectral characteristics embedded in the dataset to achieve pixel-level atmospheric correction.The study demonstrate that the proposed method achieves an average absolute error of 0.028 and a relative error of 0.82 compared to pixel-wise correction using radiative transfer models,while improving processing speed by approximately six orders of magnitude.Compared with mainstream physical-model-based methods in terms of image quality,quantitative metrics,and consistency with in-situ measured vegetation spectra,the proposed method performs favorably.This research provides an efficient atmospheric correction pathway for the quantitative application of multi-spectral remote sensing imagery.
作者
余雨棠
余涛
谢东海
吴俣
张丽丽
左欣
YU Yutang;YU Tao;XIE Donghai;WU Yu;ZHANG Lili;ZUO Xin(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China;Demonstration Center for Spaceborne Remote Sensing,China National Space Administration,Beijing 100101,China;College of Resource Environment and Tourism,Capital Normal University,Beijing 100089,China;School of Earth System Science,Tianjin University,Tianjin 300072,China;Beijing Institute of Space Mechanics&Electricity,Beijing 100094,China)
出处
《航天返回与遥感》
北大核心
2025年第4期101-115,共15页
Spacecraft Recovery & Remote Sensing
基金
国家重点研发计划(2022YFB3902200)
国家自然科学基金(42071318)
天津市自然科学基金(24JCZDJC01120)。
关键词
多光谱遥感
数据驱动大气校正
地表反射率
辐射传输模型
神经网络
multi-spectral remote sensing
data-driven atmospheric correction
surface reflectance
radiative transfer model
neural network
