论文标题
rdanet：一种基于深度学习的合成孔径雷达图像形成的方法
RDAnet: A Deep Learning Based Approach for Synthetic Aperture Radar Image Formation
论文作者
论文摘要
合成孔径雷达（SAR）成像系统通过从移动物体（例如卫星）向感兴趣的目标发射雷达信号来运行。收到反射的雷达回声，然后由图像形成算法使用以形成SAR图像。在计算机视觉任务（例如分类或自动目标识别）中使用SAR图像非常感兴趣。但是，如今，SAR应用程序由多个操作组成：图像形成，然后是图像处理。在这项工作中，我们训练一个深层神经网络，该网络同时执行图像形成和图像处理任务，从而集成SAR处理管道。结果表明，我们的集成管道可以用与传统算法形成的图像质量准确地输出分类的SAR图像。我们认为，这项工作是使用真实数据的基于神经网络的SAR处理管道的首次演示。
Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar signals from a moving object, such as a satellite, towards the target of interest. Reflected radar echoes are received and later used by image formation algorithms to form a SAR image. There is great interest in using SAR images in computer vision tasks such as classification or automatic target recognition. Today, however, SAR applications consist of multiple operations: image formation followed by image processing. In this work, we train a deep neural network that performs both the image formation and image processing tasks, integrating the SAR processing pipeline. Results show that our integrated pipeline can output accurately classified SAR imagery with image quality comparable to those formed using a traditional algorithm. We believe that this work is the first demonstration of an integrated neural network based SAR processing pipeline using real data.
