先验引导的多降质特征“珞珈一号”夜光遥感影像质量提升方法  

A priori guided method for improving the quality of Luojia01-1 NTL image with multiple degradation features

作　　者：卜丽静 吴文玉 张正鹏 杨银[3,4] BU Lijing;WU Wenyu;ZHANG Zhengpeng;YANG Yin(College of Automation and Electronic Information,Xiangtan University,Xiangtan 411105,China;Twenty First Century Aerospace Technology Co.,Ltd.,Beijing 100096,China;School of Mathematics and Computational Science,Xiangtan University,Xiangtan 411105,China;National Center for Applied Mathematics in Hunan,Xiangtan 411105,China)

机构地区：[1]湘潭大学自动化与电子信息学院,湖南湘潭411105 [2]二十一世纪空间技术应用股份有限公司,北京100096 [3]湘潭大学数学与计算机科学学院,湖南湘潭411105 [4]湖南国家应用数学中心,湖南湘潭411105

出　　处：《测绘学报》2023年第2期244-259,共16页Acta Geodaetica et Cartographica Sinica

基　　金：国家重点研发计划(2020YFA0713503);湖南省自然资源厅项目(2022-15);湖南省科学技术厅面上项目(2022JJ30561)。

摘　　要：珞珈一号夜光(NTL)遥感影像存在云雾、辉光、分辨率降低等多种复杂的降质现象,现有的深度学习影像质量提升网络往往只针对某一种类型的降质问题,且没有充分利用影像的先验信息,训练和学习过程的可解释性差,去除的降质类型单一。因此,针对含有多种复杂降质的影像质量提升问题,提出一种可解释性先验引导的多降质特征夜光影像质量提升方法。分析降质过程和降质表现,推导出了辉光、云雾噪声和空间分辨率下降的综合降质模型,并以该模型作为先验引导构建了云雾、辉光、分辨率3类数据集。在网络结构方面,针对3类降质设计了包含通道注意力模块和像素注意力模块的残差密连卷积神经网络,并用比值稀疏约束损失函数进一步提高影像的清晰度。利用珞珈一号夜光遥感影像进行了试验,结果表明,本文方法可有效去除云雾、辉光等的影响,处理后影像中的灯光边缘信息更加清晰,空间分辨率提高,影像质量提升明显。There are many complex degradation phenomena in nighttime light remote sensing image of Luojia01-1,such as cloud,glow,reduced resolution,and so on.The existing deep learning-based networks for image quality improvement often only aim at a certain type of degradation problems,and do not make full use of the priori information of the image,the interpretability of the training and learning process is poor,and the type of degradation removed is single.Therefore,aiming at the problem of image quality improvement with multiple complex degradation features,an interpretable a priori guided nighttime light remote sensing image quality improvement method with multiple degradation features is proposed.Firstly,the degradation process and performance are analyzed,and a comprehensive degradation model of glow,cloud noise,and spatial resolution degradation is derived.Taking the model as a priori guide,three kinds of data sets of cloud glow,and resolution are constructed.In terms of network structure,for three kinds of degradation,a residual dense convolution neural network including channel attention module and pixel attention module is designed,and the ratio sparse constraint loss function is used to further improve the clarity of the image.The experiment is carried out with the nighttime light remote sensing image of Luojia01-1.The results show that the method proposed in this paper can effectively remove the influence of cloud and fog and glow.After processed,the image light edge information is clearer,the spatial resolution is improved,and the image quality is improved obviously.

关 键 词：夜光遥感 影像质量提升 深度学习 去云雾 降质模型 超分辨率重建 

分 类 号：P237[天文地球—摄影测量与遥感]