太阳成8722(中国)有限公司-GREEN NO.1

标题: FDFNet: A Fusion Network for Generating High-Resolution Fully PolSAR Images

作者: Lin, LP (Lin, Liupeng); Shen, HF (Shen, Huanfeng); Li, J (Li, Jie); Yuan, QQ (Yuan, Qiangqiang)

来源出版物: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 卷: 19 文献号: 4500905 DOI: 10.1109/LGRS.2021.3127958 出版年: 2022

摘要: Deep learning shows potential superiority in the image fusion field. To solve the problem of the spatial resolution degradation of polarimetric synthetic aperture radar (PolSAR) images caused by system limitation, we propose a fully PolSAR images and DualSAR images fusion network (FDFNet). We use low resolution (LR)-PolSAR super-resolution (LPSR) and modified cross attention mechanism (MCroAM) to perform data fusion on LR-PolSAR and high resolution (HR)-dual-polarization synthetic aperture radar (DualSAR) and design a polarimetric decomposition attention module to introduce the polarimetric parameters of LR-PolSAR images to maintain polarimetric information. Besides, we use the differential information between LR-PolSAR and HR-DualSAR to guide spatial resolution reconstruction. The loss function based on the L-1 norm is used to constrain the network training process. The experimental results show the superiority of the proposed method over the existing methods in visual and quantitative evaluation. In addition, polarimetric decomposition experiments verify the effectiveness of the proposed method to maintain polarimetric information.

作者关键词: Differential information; dual-polarization synthetic aperture radar (DualSAR); fully-polarimetric synthetic aperture radar (PolSAR); fusion; polarimetric decomposition

地址: [Lin, Liupeng; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Shen, Huanfeng; Yuan, Qiangqiang] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

[Li, Jie; Yuan, Qiangqiang] Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China.

通讯作者地址: Li, J (通讯作者)，Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China.

电子邮件地址: linliupeng@whu.edu.cn; shenhf@whu.edu.cn; aaronleecool@whu.edu.cn; qqyuan@sgg.whu.edu.cn

影响因子：3.966