Fully Polarimetric SAR and Single-Polarization SAR Image Fusion Network
This work addresses a domain-specific issue in remote sensing by improving image resolution for PolSAR data, which is incremental as it builds on existing fusion techniques with novel components.
The paper tackles the problem of reduced resolution in polarimetric synthetic aperture radar (PolSAR) images by proposing a fusion network that combines low-resolution PolSAR with high-resolution single-polarization SAR to generate high-resolution PolSAR images, achieving an average PSNR increase of over 3.6 dB and reducing average MAE to below 0.07.
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.