Next Article in Journal
Cross-Domain Building Models—A Step towards Interoperability
Next Article in Special Issue
Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification
Previous Article in Journal
Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts
Previous Article in Special Issue
Representative Band Selection for Hyperspectral Image Classification
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(9), 362; https://doi.org/10.3390/ijgi7090362

Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features

1
,
1,* , 2,3,* , 4
and
1
1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
Laboratory for Environment Computation & Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
3
Nuclear Industry Huzhou Engineering Investigation Institute, Huzhou 313000, China
4
Department of Land Surveying and Geo-Information, The Hong Kong Polytechnic University, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Received: 8 August 2018 / Revised: 26 August 2018 / Accepted: 31 August 2018 / Published: 2 September 2018
Full-Text   |   PDF [5712 KB, uploaded 7 September 2018]   |  

Abstract

Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images. View Full-Text
Keywords: edge constraints; Gabor features; object segmentation; region growing; road extraction; shape features edge constraints; Gabor features; object segmentation; region growing; road extraction; shape features
Figures

Graphical abstract

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, L.; Zhu, Q.; Xie, X.; Hu, H.; Zeng, H. Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features. ISPRS Int. J. Geo-Inf. 2018, 7, 362.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top