论文标题
SAR2SAR：SAR Images的半监督幻想算法
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
论文作者
论文摘要
减少斑点是许多遥感应用程序中的关键步骤。通过强烈影响合成孔径雷达（SAR）图像，它使它们难以分析。由于难以对斑点的空间相关性进行建模，因此在本文中提出了一种与自学的深度学习算法：SAR2SAR。多个时间序列序列是利用的，神经网络仅通过查看嘈杂的获取来恢复SAR图像。为此，已采用了最近提出的噪声2Noise框架。根据时间变化的补偿和适应斑点统计数据的损失功能的补偿，提出了使其适应SAR Despeckling的策略。 提出了一项用于合成斑点噪声的研究，以将所提出方法的性能与其他最先进的过滤器进行比较。然后，讨论了真实图像的结果，以显示所提出的算法的潜力。该代码可用于该领域的测试和可重复研究。
Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. Multi-temporal 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 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.
