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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Road Segmentation in SAR Satellite Images With Deep Fully Convolutional Neural Networks
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Road Segmentation in SAR Satellite Images With Deep Fully Convolutional Neural Networks

机译：深度全卷积神经网络的SAR卫星图像道路分割

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摘要

Remote sensing is extensively used in cartography. As transportation networks grow and change, extracting roads automatically from satellite images is crucial to keep maps up-to-date. Synthetic aperture radar (SAR) satellites can provide high-resolution topographical maps. However, roads are difficult to identify in these data as they look visually similar to targets, such as rivers and railways. Most road extraction methods on SAR images still rely on a prior segmentation performed by the classical computer vision algorithms. Few works study the potential of deep learning techniques, despite their successful applications to optical imagery. This letter presents an evaluation of fully convolutional neural networks (FCNNs) for road segmentation in SAR images. We study the relative performance of early and state-of-the-art networks after carefully enhancing their sensitivity toward thin objects by adding the spatial tolerance rules. Our models show promising results, successfully extracting most of the roads in our test data set. This shows that although FCNNs natively lack efficiency for road segmentation, they are capable of good results if properly tuned. As the segmentation quality does not scale well with the increasing depth of the networks, the design of specialized architectures for roads extraction should yield better performances.
机译：遥感技术广泛应用于制图。随着交通网络的发展和变化，从卫星图像自动提取道路对于保持地图的最新状态至关重要。合成孔径雷达（SAR）卫星可以提供高分辨率的地形图。但是，在这些数据中很难识别道路，因为它们在外观上与目标（例如河流和铁路）相似。 SAR图像上的大多数道路提取方法仍然依赖于经典计算机视觉算法执行的事先分割。尽管深度学习技术已成功应用于光学图像，但很少有作品能够研究深度学习技术的潜力。这封信提出了对用于SAR图像道路分割的全卷积神经网络（FCNN）的评估。在通过添加空间容限规则仔细提高了它们对薄物体的敏感性之后，我们研究了早期和最先进网络的相对性能。我们的模型显示出令人鼓舞的结果，成功提取了我们测试数据集中的大部分道路。这表明，尽管FCNN本身缺乏道路分割的效率，但如果进行适当的调整，它们仍可以取得良好的效果。由于分割质量不能随着网络深度的增加而很好地扩展，因此用于道路提取的专用体系结构的设计应产生更好的性能。

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