SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images
SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images
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DOI：
10.1109/jstars.2021.3071864
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发表时间：
2021
影响因子：
5.5
通讯作者：
Emanuele Dalsasso;L. Denis;F. Tupin
中科院分区：
文献类型：
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作者：
Emanuele Dalsasso;L. Denis;F. Tupin
在许多遥感应用中，斑点减少是一个关键步骤。由于对合成孔径雷达（SAR）图像的强烈影响，使其难以分析。针对散斑空间相关性难以建模的问题，本文提出了一种半监督的深度学习算法：SAR2SAR。利用多时间序列，神经网络学习仅通过查看噪声获取来恢复SAR图像。为此，我们采用了最近提出的noise2noise框架[1]。提出了一种基于时间变化补偿和适合散斑统计的损失函数的SAR去斑处理策略。通过对合成散斑噪声的研究，比较了该方法与其他先进滤波器的性能。然后，讨论了实际图像的结果，以显示该算法的潜力。提供代码是为了允许在该领域进行测试和可重复的研究。
Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with semi-supervision is proposed in this article: SAR2SAR. Multitemporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework [1] has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field.