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

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.