  The data fusion technology aims to aggregate the characteristics of different
data and obtain products with multiple data advantages. To solves the problem
of reduced resolution of PolSAR images due to system limitations, we propose a
fully polarimetric synthetic aperture radar (PolSAR) images and
single-polarization synthetic aperture radar SAR (SinSAR) images fusion network
to generate high-resolution PolSAR (HR-PolSAR) images. To take advantage of the
polarimetric information of the low-resolution PolSAR (LR-PolSAR) image and the
spatial information of the high-resolution single-polarization SAR (HR-SinSAR)
image, we propose a fusion framework for joint LR-PolSAR image and HR-SinSAR
image and design a cross-attention mechanism to extract features from the joint
input data. Besides, based on the physical imaging mechanism, we designed the
PolSAR polarimetric loss function for constrained network training. The
experimental results confirm the superiority of fusion network over traditional
algorithms. The average PSNR is increased by more than 3.6db, and the average
MAE is reduced to less than 0.07. Experiments on polarimetric decomposition and
polarimetric signature show that it maintains polarimetric information well.
