基于卷积神经网络的太阳光谱辐照度超分辨率重建方法  

Super-resolution Solar Spectral Irradiance Reconstruction Method Based on Convolutional Neural Network

作　　者：张鹏 翁建文 康晴[2] 李健军[2] ZHANG Peng;WENG Jianwen;KANG Qing;LI Jianjun(School of Physical Sciences,University of Science and Technology of China,Hefei 230026,China;Key Laboratory of Optical Calibration and Characterization,Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Hefei 230031,China)

机构地区：[1]中国科学技术大学物理学院,合肥230026 [2]中国科学院安徽光学精密机械研究所中国科学院通用光学定标与表征技术重点实验室,合肥230031

出　　处：《光子学报》2025年第3期221-230,共10页Acta Photonica Sinica

基　　金：国家重点研发计划(No.2022YFB3902901);国家自然科学基金(No.42105121)。

摘　　要：针对现有天基参考太阳光谱辐照度数据分辨率不足,限制其应用范围的问题,提出一种基于卷积神经网络的太阳光谱辐照度超分辨率重建方法。该网络由一个基于物理模型的全连通层、一维卷积层、非线性层和一系列具有跳跃连接的残差网络组成。同时考虑现有均方误差损失函数无法捕捉太阳光谱峰谷特征的问题,提出将光谱相对于波长的一阶、二阶导数加入损失函数,使残差的特征更集中于关键的光谱内容。将所提方法应用于TSIS-1 SIM测量太阳光谱辐照度可见光波段的超分辨率重建,结果表明该方法重建光谱与TSIS-1 HSRS产品的测量结果质量相当,且重建耗时仅需0.9421 s,可有效提高天基观测太阳光谱辐照度数据的分辨率。Obtaining accurate,resolved and traceable reference solar spectral irradiance variations is of great research significance and application value in the fields of solar physics,atmospheric physics and environmental science.However,the high-precision solar spectral irradiance data available domestically and internationally generally has a low resolution,while the high-resolution reference solar spectral irradiance has a low precision,and the acquisition of high-resolution solar spectral irradiance data usually faces the problems of sampling difficulty,time-consuming sampling,and limited data precision.To address this problem,we propose a deep learning-based approach to reconstruct high-resolution spectral irradiance by analyzing a large amount of low-resolution spectral irradiance data.Our approach is based on a novel end-to-end fully convolutional residual neural network architecture that employs a new loss function,and by training the CNN model,we can learn the spectral features of the solar radiation to achieve high-resolution reconstruction of the solar spectral irradiance.This method utilizes the advantage of CNN in spectral feature extraction,which can fully exploit the feature information of high-resolution spectra.In our experiments,we first select the visible band(311.4~949.4 nm)of the HSRS high-resolution spectral dataset to add noise to expand the data to 5000 spectra,and then convolve all the data with the Line Shape Function(LSF)with the SIM instrument to resample them into spectral data consistent with the low-resolution spectral resolution of the TSIS-1 SIM.Our CNN model is designed with some key improvement strategies to better accommodate the feature extraction requirements of high-resolution spectral reconstruction.The CNN spectral super-resolution network consists of a fully connected layer,a one-dimensional convolutional layer,a nonlinear layer,eight residual blocks,a one-dimensional convolutional layer and a cascade of nonlinear layers.The network was trained and tested on an Intel i7-12650H 2.

关 键 词：高分辨率 太阳光谱辐照度 卷积神经网络 残差网络 光谱超分辨率重建 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]