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Remote Sens. 2019, 11(8), 930; https://doi.org/10.3390/rs11080930

Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
2
Computer Science Department, Xi’an Jiaotong University, Xi’an 710049, China
3
Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Received: 19 February 2019 / Revised: 1 April 2019 / Accepted: 9 April 2019 / Published: 17 April 2019
(This article belongs to the Special Issue Deep Transfer Learning for Remote Sensing)
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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. View Full-Text
Keywords: deep learning; road extraction; generative adversarial network deep learning; road extraction; generative adversarial network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, X.; Han, X.; Li, C.; Tang, X.; Zhou, H.; Jiao, L. Aerial Image Road Extraction Based on an Improved Generative Adversarial Network. Remote Sens. 2019, 11, 930.

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