PolMERLIN：基于掩码网络的自监督极化复数SAR图像去斑
PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks
AI总结：
本文提出PolMERLIN，通过通道掩码和空间掩码的自监督方法，将单极化SAR去斑模型扩展至多极化场景，在合成与真实数据上均超越现有最优方法。
摘要：
去斑是提升合成孔径雷达（SAR）图像质量的关键降噪任务。直接获取无噪声SAR图像十分困难，这阻碍了精确去斑算法的发展。深度学习的出现推动了仅从含噪SAR图像中学习的去噪模型研究。然而，现有方法仅处理单极化图像，无法应对现代卫星捕获的多极化图像。本文对现有生成单极化SAR图像的模型进行扩展，以处理多极化SAR图像。具体地，我们提出了一种新颖的自监督去斑方法，称为通道掩码，利用极化之间的关系。此外，我们采用空间掩码方法来处理像素间相关性，进一步提升方法性能。通过有效融合多极化信息，我们的方法在合成场景和真实场景的定量评估中均超越了当前最先进的方法。
英文摘要：
Despeckling is a crucial noise reduction task in improving the quality of synthetic aperture radar (SAR) images. Directly obtaining noise-free SAR images is a challenging task that has hindered the development of accurate despeckling algorithms. The advent of deep learning has facilitated the study of denoising models that learn from only noisy SAR images. However, existing methods deal solely with single-polarization images and cannot handle the multi-polarization images captured by modern satellites. In this work, we present an extension of the existing model for generating single-polarization SAR images to handle multi-polarization SAR images. Specifically, we propose a novel self-supervised despeckling approach called channel masking, which exploits the relationship between polarizations. Additionally, we utilize a spatial masking method that addresses pixel-to-pixel correlations to further enhance the performance of our approach. By effectively incorporating multiple polarization information, our method surpasses current state-of-the-art methods in quantitative evaluation in both synthetic and real-world scenarios.
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