A CNN-Based Self-Supervised Synthetic Aperture Radar Image Denoising Approach

Tan, S ORCID: https://orcid.org/0000-0001-5372-7850, Zhang, X ORCID: https://orcid.org/0000-0001-7844-593X, Wang, H, Yu, L ORCID: https://orcid.org/0000-0003-3115-2042, Du, Y ORCID: https://orcid.org/0000-0002-0143-7621, Yin, J and Wu, B ORCID: https://orcid.org/0000-0001-5546-365X (2022) A CNN-Based Self-Supervised Synthetic Aperture Radar Image Denoising Approach. IEEE Transactions on Geoscience and Remote Sensing, 60. 5213615. ISSN 0196-2892

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Abstract

Synthetic aperture radar (SAR) plays an essential role in earth observation and projection due to its capability to penetrate clouds, which makes it possible to monitor terrestrial surfaces under all weather conditions. Multiplicative noise often occurs in the SAR signal, hampering the retrieval of information from SAR imagery. Convolutional neural networks (CNNs) have been used in many computer vision tasks and are helpful in image denoising. However, current CNN-based denoising approaches inevitably lead to a “washed out” effect that loses spatial details. Another limitation is that most typical CNN-based denoising models require a noise-free image for training. To address these issues, we propose a novel end-to-end self-supervised SAR denoising model: Enhanced Noise2Noise (EN2N), which can be trained without a noise-free image. To enhance the quality of the result images, the perceptual features from a pre-learned CNN are introduced to restore the spatial details by a hybrid loss function. Experiments show that our proposed method outperforms the typical denoising methods in terms of noise reduction and feature preservation based on image quality metrics. Also, the new hybrid loss could enhance the spatial details significantly. The good performance maintains the robustness throughout time, which reduces the uncertainty in time-series SAR caused by random noise. Benefiting from optimization of graphics processing unit (GPU) and multi-threading, the proposed method has higher computation efficiency than traditional methods. This study demonstrates the great potential of using our self-supervised deep learning approaches for SAR image denoising in the future.