Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

Xiangrong Zhang*, Xiao Han, Chen Li, Xu Tang, Huiyu Zhou, Licheng Jiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
1 Downloads (Pure)

Abstract

Aerial photographs and satellite images are one of the resources used for earth observation. In practice, automated detection of roads on aerial images is of significant values for the application such as car navigation, law enforcement, and fire services. In this paper, we present a novel road extraction method from aerial images based on an improved generative adversarial network, which is an end-to-end framework only requiring a few samples for training. Experimental results on the Massachusetts Roads Dataset show that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision and F1-score.

Original languageEnglish
Article number930
Number of pages19
JournalRemote Sensing
Volume11
Issue number8
DOIs
Publication statusPublished - 17 Apr 2019
Externally publishedYes

Bibliographical note

Funding Information:
Funding: This research was funded by the National Natural Science Foundation of China under Grant 61772400, Grant 61801351, Grant 61501353, Grant 61772399, and Grant 61573267. H. Zhou was supported by UK EPSRC under Grant EP/N011074/1, Royal Society Newton Advanced Fellowship under Grant NA160342, and European Union’s Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement no.720325. The APC was funded by the National Natural Science Foundation of China under Grant 61772400, Grant 61501353, Grant 61772399, and Grant 61573267.

Publisher Copyright:
© 2019 by the authors.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • Deep learning
  • Generative adversarial network
  • Road extraction

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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