PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling
  with Masked Networks
Shunya Kato Masaki Saito Katsuhiko Ishiguro Sol Cummings
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.