Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

被引：50

作者：

Zhang, Xiangrong ^{[1
]}

Han, Xiao ^{[1
]}

Li, Chen ^{[2
]}

Tang, Xu ^{[1
]}

Zhou, Huiyu ^{[3
]}

Jiao, Licheng ^{[1
]}

机构：

[1] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Joint Int Res Lab Intelligent Percept & Computat, Sch Artificial Intelligence,Minist Educ,Int Res C, Xian 710071, Shaanxi, Peoples R China

[2] Xi An Jiao Tong Univ, Dept Comp Sci, Xian 710049, Shaanxi, Peoples R China

[3] Univ Leicester, Dept Informat, Leicester LE1 7RH, Leics, England

来源：

REMOTE SENSING | 2019年 / 11卷 / 08期

基金：

英国工程与自然科学研究理事会; 中国国家自然科学基金; 欧盟地平线“2020”;

关键词：

deep learning; road extraction; generative adversarial network; RESOLUTION SATELLITE IMAGES; CENTERLINE EXTRACTION; FEATURES; CLASSIFICATION; SCALE;

D O I：

10.3390/rs11080930

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：19

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