用深度学习分析雷达图像，能准确识别地物形状和冰层类型。
A machine learning approach for image classification in synthetic aperture RADAR
- 用卷积神经网络处理模拟与真实雷达数据
- 对地物形状和冰层类型的分类准确率超75%
- 验证了不同天线高度对识别效果的影响
本文研究利用卷积神经网络（CNN）对合成孔径雷达（SAR）图像中的地面目标进行识别与分类。通过单散射近似，我们基于模拟的SAR数据和由此重建的图像对物体形状进行分类，并比较两种方法的效果。随后，我们在真实SAR影像（来自卫星Sentinel-1）上识别冰层类型。两项实验均取得超过75%的高分类准确率。结果表明，CNN在处理SAR数据完成几何与环境分类任务方面具有显著有效性。此外，研究还探讨了不同天线高度对目标分类能力的影响。
原文摘要 · Abstract (English)
We consider the problem in Synthetic Aperture RADAR (SAR) of identifying and classifying objects located on the ground by means of Convolutional Neural Networks (CNNs). Specifically, we adopt a single scattering approximation to classify the shape of the object using both simulated SAR data and reconstructed images from this data, and we compare the success of these approaches. We then identify ice types in real SAR imagery from the satellite Sentinel-1. In both experiments we achieve a promising high classification accuracy ($\geq$75\%). Our results demonstrate the effectiveness of CNNs in using SAR data for both geometric and environmental classification tasks. Our investigation also explores the effect of SAR data acquisition at different antenna heights on our ability to classify objects successfully.
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