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作者:

Wang, Zhuozheng (Wang, Zhuozheng.) | Zhang, Meng (Zhang, Meng.) | Liu, Wei (Liu, Wei.)

收录:

EI

摘要:

In the field of remote sensing imagery, road extraction is one of the key technologies supporting for Landuse Landcover classification. In this paper, a new semantic segmentation neural network named SAT U-Net is proposed for road extraction from remote sensing imagery. The new improved network replaces the sigmoid layer in the U-Net with a self-adaptive threshold method proposed to self-adaptively adjust the road thresholds for segmentation results of U-Net. The proposed method is combined with the strength of U-Net architecture to retain the complete road spatial features, thus overcomes the problem of unconnected and blurry roads in the segmentation results. To prove the effectiveness and utility of the proposed network, it was experimented on the test set of a public road dataset and compared with U-Net in five different environments. Experimental results demonstrate that the proposed method is superior to U-Net and presents clearer and more complete road structures. © 2019 IEEE.

关键词:

Extraction Feature extraction Image segmentation Remote sensing Roads and streets Semantics Statistical tests

作者机构:

  • [ 1 ] [Wang, Zhuozheng]Faculty of Information Technology, Beijing, China
  • [ 2 ] [Wang, Zhuozheng]Intelligent Signal Processing Laboratory, Beijing University of Technology, Beijing, China
  • [ 3 ] [Zhang, Meng]Faculty of Information Technology, Beijing, China
  • [ 4 ] [Zhang, Meng]Intelligent Signal Processing Laboratory, Beijing University of Technology, Beijing, China
  • [ 5 ] [Liu, Wei]Faculty of Information Technology, Beijing, China
  • [ 6 ] [Liu, Wei]Intelligent Signal Processing Laboratory, Beijing University of Technology, Beijing, China

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年份: 2019

页码: 455-460

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

近30日浏览量: 3

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