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International Conference on Wireless Algorithms, Systems, and Applications

WASA 2021: Wireless Algorithms, Systems, and Applications pp 457–468Cite as

DDCAttNet: Road Segmentation Network for Remote Sensing Images

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12938))

Abstract

Semantic segmentation of remote sensing images based on deep convolutional neural networks has proven its effectiveness. However, due to the complexity of remote sensing images, deep convolutional neural networks have difficulties in segmenting objects with weak appearance coherences even though they can represent local features of object effectively. The road networks segmentation of remote sensing images faces two major problems: high inter-individual similarity and ubiquitous occlusion. In order to address these issues, this paper develops a novel method to extract roads from complex remote sensing images. We designed a Dual Dense Connected Attention network (DDCAttNet) that establishes long-range dependencies between road features. The architecture of the network is designed to incorporate both spatial attention and channel attention information into semantic segmentation for accurate road segmentation. Experimental results on the benchmark dataset demonstrate the superiority of our proposed approach both in quantitative and qualitative evaluation.

This research was supported in part by National Key Research and Development Plan Key Special Projects under Grant No. 2018YFB2100303, Shandong Province colleges and universities youth innovation technology plan innovation team project under Grant No. 2020KJN011, Shandong Provincial Natural Science Foundation under Grant No. ZR2020MF060, Program for Innovative Postdoctoral Talents in Shandong Province under Grant No. 40618030001, National Natural Science Foundation of China under Grant No. 61802216, and Postdoctoral Science Foundation of China under Grant No. 2018M642613.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China

    Genji Yuan, Jianbo Li, Yinong Li & Zhihao Xu

  2. Institute of Ubiquitous Networks and Urban Computing, Qingdao, 266070, China

    Jianbo Li & Zhiqiang Lv

Authors
  1. Genji Yuan
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  2. Jianbo Li
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  3. Zhiqiang Lv
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  4. Yinong Li
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  5. Zhihao Xu
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Correspondence to Jianbo Li .

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Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhe Liu

  2. Shanghai Jiao Tong University, Shanghai, China

    Fan Wu

  3. Missouri University of Science and Technology, Rolla, MO, USA

    Sajal K. Das

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Yuan, G., Li, J., Lv, Z., Li, Y., Xu, Z. (2021). DDCAttNet: Road Segmentation Network for Remote Sensing Images. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12938. Springer, Cham. https://doi.org/10.1007/978-3-030-86130-8_36

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  • DOI: https://doi.org/10.1007/978-3-030-86130-8_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86129-2

  • Online ISBN: 978-3-030-86130-8

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