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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Learning From GPS Trajectories of Floating Car for CNN-Based Urban Road Extraction With High-Resolution Satellite Imagery
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Learning From GPS Trajectories of Floating Car for CNN-Based Urban Road Extraction With High-Resolution Satellite Imagery

机译：高分辨率卫星图像从浮动车GPS轨迹学习

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摘要

Deep learning has achieved great success in recent years, among which the convolutional neural network (CNN) method is outstanding in image segmentation and image recognition. It is also widely used in satellite imagery road extraction and, generally, can obtain accurate and extraction results. However, at present, the extraction of roads based on CNN still requires a lot of manual preparation work, and a large number of samples can be marked to achieve extraction, which has to take long drawing cycle and high production cost. In this article, a new CNN sample set production method is proposed, which uses the GPS trajectories of floating car as training set (GPSTasST), for the multilevel urban roads extraction from high-resolution remote sensing imagery. This method rasterizes the GPS trajectories of floating car into a raster map and uses the processed raster map to label the satellite image to obtain a road extraction sample set. CNN can extract roads from remote sensing imagery by learning the training set. The results show that the method achieves a harmonic mean of precision and recall higher than road extraction method from single data source while eliminating the manual labeling work, which shows the effectiveness of this work.
机译：近年来，深度学习取得了巨大的成功，其中卷积神经网络（CNN）方法在图像分割和图像识别中出现突出。它也广泛用于卫星图像道路提取，一般来说，可以获得准确和提取结果。然而，目前，基于CNN的道路提取仍需要大量的手动制备工作，并且可以标记大量样品以实现萃取，这必须采用长绘图周期和高生产成本。在本文中，提出了一种新的CNN样本集生产方法，它使用浮动汽车的GPS轨迹作为训练集（GPSTASST），对于来自高分辨率遥感图像的多级城市道路提取。该方法将浮动车的GPS轨迹塑造成光栅映射，并使用处理的光栅映射来标记卫星图像以获得道路提取样品集。 CNN可以通过学习训练集来提取来自遥感图像的道路。结果表明，该方法达到了比单个数据源的精度和召回高于道路提取方法的谐波平均值，同时消除了手动标签工作，这表明了这项工作的有效性。

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