PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling
with Masked Networks
- URL: http://arxiv.org/abs/2401.07503v1
- Date: Mon, 15 Jan 2024 07:06:36 GMT
- Title: PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling
with Masked Networks
- Authors: Shunya Kato, Masaki Saito, Katsuhiko Ishiguro, Sol Cummings
- Abstract summary: Despeckling is a crucial noise reduction task in improving the quality of synthetic aperture radar (SAR) images.
Existing methods deal solely with single-polarization images and cannot handle the multi-polarization images captured by modern satellites.
We propose a novel self-supervised despeckling approach called channel masking, which exploits the relationship between polarizations.
- Score: 2.580765958706854
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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