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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Road Segmentation Based on Hybrid Convolutional Network for High-Resolution Visible Remote Sensing Image
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Road Segmentation Based on Hybrid Convolutional Network for High-Resolution Visible Remote Sensing Image

机译：基于混合卷积网络的高分辨率可视化道路分割

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摘要

Road segmentation plays an important role in many applications, such as intelligent transportation system and urban planning. Various road segmentation methods have been proposed for visible remote sensing images, especially the popular convolutional neural network-based methods. However, high-accuracy road segmentation from high-resolution visible remote sensing images is still a challenging problem due to complex background and multiscale roads in these images. To handle this problem, a hybrid convolutional network (HCN), fusing multiple subnetworks, is proposed in this letter. The HCN contains a fully convolutional network, a modified U-Net, and a VGG subnetwork; these subnetworks obtain a coarse-grained, a medium-grained, and a fine-grained road segmentation map. Moreover, the HCN uses a shallow convolutional subnetwork to fuse these multigrained segmentation maps for final road segmentation. Benefitting from multigrained segmentation, our HCN shows impressing results in processing both multiscale roads and complex background. Four testing indicators, including pixel accuracy, mean accuracy, mean region intersection over union (IU), and frequency weighted IU, are computed to evaluate the proposed HCN on two testing data sets. Compared with five state-of-the-art road segmentation methods, our HCN has higher segmentation accuracy than them.
机译：道路分割在许多应用中都起着重要作用，例如智能交通系统和城市规划。已经提出了用于可见遥感图像的各种道路分割方法，特别是基于流行的基于卷积神经网络的方法。然而，由于这些图像中复杂的背景和多尺度道路，从高分辨率的可见遥感图像中进行高精度道路分割仍然是一个具有挑战性的问题。为了解决这个问题，在这封信中提出了一种融合了多个子网的混合卷积网络（HCN）。 HCN包含一个完整的卷积网络，一个修改的U-Net和一个VGG子网；这些子网会获得粗粒度，中粒度和细粒度的道路分割图。此外，HCN使用浅层卷积子网融合这些多粒度分割图以进行最终道路分割。得益于多颗粒分割，我们的HCN在处理多尺度道路和复杂背景方面均显示出令人印象深刻的结果。计算了四个测试指标，包括像素精度，平均精度，平均联合区域交集（IU）和频率加权IU，以在两个测试数据集上评估建议的HCN。与五种最新的道路分割方法相比，我们的HCN的分割精度更高。

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  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2019年第4期|613-617|共5页
  • 作者

    Li Ye; Guo Lili; Rao Jun; Xu Lele; Jin Shan;

  • 作者单位

    Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing, Peoples R China;

    Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China|Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing, Peoples R China;

    Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing, Peoples R China;

    Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Convolutional neural network (CNN); high-resolution visible remote sensing image; road segmentation;

    机译：卷积神经网络（CNN）;高分辨率可见遥感图像;道路分割;

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