只需投影！多通道去斑的简单方法
Just Project! Multi-Channel Despeckling, the Easy Way
AI总结：
针对多通道SAR图像去斑难题，本文提出MuChaPro框架，通过生成、恢复并重组大量单通道投影，复用现有单通道方法，并支持自监督训练传感器专属网络。
摘要：
降低多通道SAR图像中的斑点波动，在SAR成像的许多应用中至关重要，例如极化分类或干涉测高。尽管单通道去斑已广泛受益于深度学习技术的应用，但将其扩展到多通道SAR图像却更具挑战性。本文介绍了MuChaPro，这是一个利用现有单通道去斑方法的通用框架。其核心思想是生成大量单通道投影，恢复这些投影，然后将它们重组为最终的多通道估计结果。这一简单方法在极化和/或干涉模态中被证明是有效的。MuChaPro的一个特别吸引力在于，可以应用自监督训练策略来学习用于单通道去斑的传感器特定网络。
英文摘要：
Reducing speckle fluctuations in multi-channel SAR images is essential in many applications of SAR imaging such as polarimetric classification or interferometric height estimation. While single-channel despeckling has widely benefited from the application of deep learning techniques, extensions to multi-channel SAR images are much more challenging. This paper introduces MuChaPro, a generic framework that exploits existing single-channel despeckling methods. The key idea is to generate numerous single-channel projections, restore these projections, and recombine them into the final multi-channel estimate. This simple approach is shown to be effective in polarimetric and/or interferometric modalities. A special appeal of MuChaPro is the possibility to apply a self-supervised training strategy to learn sensor-specific networks for single-channel despeckling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
