Road Network Extraction From Low-Contrast SAR Images

被引：5

作者：

Zeng, Tao ^{[1
,2
]}

Gao, Qiang ^{[1
,2
]}

Ding, Zegang ^{[1
,2
]}

Chen, Jing ^{[1
,2
]}

Li, Gen ^{[1
,2
]}

机构：

[1] Beijing Inst Technol, Radar Res Lab, Sch Informat & Elect, Beijing 100081, Peoples R China

[2] Beijing Key Lab Embedded Real Time Informat Proc, Beijing 100081, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2019年 / 16卷 / 06期

关键词：

Low-contrast synthetic aperture radar (SAR) image; Monte Carlo analysis; region growing (RG); road network extraction; URBAN; RESOLUTION; AREAS;

D O I：

10.1109/LGRS.2018.2889299

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

In low-contrast synthetic aperture radar (SAR) images, the contrast between roads and surrounding objects is low; therefore, many false roads will be introduced in the process of extracting road networks. To solve this problem, a two-step road network extraction framework is proposed. In the first step, the edge information of the road is extracted using a linear detector. To reduce false edges, a false edge removal algorithm based on the directional information of the edges is proposed. In the second step, an improved region growing (RG) algorithm is proposed, which can substantially improve the integrity of the road network extraction compared with the traditional RG algorithm. Finally, the proposed algorithm is validated by GF-3 satellite SAR images.

引用

页码：907 / 911

页数：5

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