Fully Polarimetric SAR and Single-Polarization SAR Image Fusion Network

TL;DR

This paper introduces a novel fusion network that combines low-resolution fully polarimetric SAR images with high-resolution single-polarization SAR images to produce high-resolution polarimetric images, enhancing resolution while preserving polarimetric information.

Contribution

It proposes a fusion framework with a cross-attention mechanism and a physical-based polarimetric loss function, improving resolution and information retention over traditional methods.

Findings

PSNR increased by over 3.6dB

MAE reduced to less than 0.07

Maintains polarimetric information effectively

Abstract

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.