Unpaired Speckle Extraction for SAR Despeckling

斑点图案 散斑噪声 人工智能 合成孔径雷达 计算机科学 卷积神经网络 计算机视觉 模式识别(心理学) 深度学习
作者
Huangxing Lin,Yihong Zhuang,Yue Huang,Xinghao Ding
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-14 被引量:3
标识
DOI:10.1109/tgrs.2022.3233892
摘要

Speckle suppression is a critical step in synthetic aperture radar (SAR) imaging. Since speckle-free SAR images are inaccessible, supervised denoising methods are not suitable for this task. To exploit the strong capabilities of convolutional neural networks (CNNs), we propose Unpaired Speckle Extraction (SAR-USE), an unsupervised method for SAR despeckling. Our method utilizes unpaired SAR and clean optical images to extract “real” speckle for learning despeckling. First, a CNN that has never seen clean SAR images is employed to extract speckle from the SAR image. Then, the extracted speckle is multiplied with a random optical image to synthesize paired data for learning speckle removal. Through a Siamese network, speckle extraction and learning despeckling are performed alternately and promote each other. To make the extracted speckle more visually and statistically realistic, it is constrained by a noise correction module to be unit mean while maintaining spatial correlation. After convergence, the CNN is a good denoiser that can effectively extract speckle from SAR images. Experiments on synthetic datasets show that the denoising ability of the proposed method is as good as its supervised counterpart. More importantly, SAR-USE is very efficient for removing the spatially correlated speckle in real data that supervised learning methods cannot.
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