AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY
Automatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy. In this...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-08-01
|
Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/113/2020/isprs-archives-XLIII-B3-2020-113-2020.pdf |
id |
doaj-9b184f4c0c3d4a519be0494b746e9b0f |
---|---|
record_format |
Article |
spelling |
doaj-9b184f4c0c3d4a519be0494b746e9b0f2020-11-25T03:14:48ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-08-01XLIII-B3-202011311710.5194/isprs-archives-XLIII-B3-2020-113-2020AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERYP. Li0Y. Li1J. Feng2Z. Ma3X. Li4Chongqing Geomatics and Remote Sensing Center, 404100 Chongqing, ChinaSchool of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, ChinaSchool of Remote Sensing and Information Engineering Engineering, Wuhan Uni versity, Wuhan,430079, ChinaChongqing Geomatics and Remote Sensing Center, 404100 Chongqing, ChinaChongqing Geomatics and Remote Sensing Center, 404100 Chongqing, ChinaAutomatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy. In this paper, a segmentation-shape analysis based method is proposed to detect road intersections and their branch directions from an image. In the region of interest, it uses the contour shape of the segmented-intersection area to form a feature vector representing its geometric information. The extracted feature vector is then matched with some template vectors in order to find the best matched intersection pattern, obtain the type of intersection and the direction of connected roads. The experimental analysis are carried out with ISPRS Vaihingen and Toronto images. The experimental results show that the proposed method can extract most of the road intersections correctly. For the Vaihingen image, the the completeness and correctness are 81% and 87%, respectfully, while for the Toronto image, the the completeness and correctness are 78% and 85%, respectfully. It can help to build more correct and complete road network.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/113/2020/isprs-archives-XLIII-B3-2020-113-2020.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
P. Li Y. Li J. Feng Z. Ma X. Li |
spellingShingle |
P. Li Y. Li J. Feng Z. Ma X. Li AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
P. Li Y. Li J. Feng Z. Ma X. Li |
author_sort |
P. Li |
title |
AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY |
title_short |
AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY |
title_full |
AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY |
title_fullStr |
AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY |
title_full_unstemmed |
AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY |
title_sort |
automatic detection and recognition of road intersections for road extraction from imagery |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2020-08-01 |
description |
Automatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy. In this paper, a segmentation-shape analysis based method is proposed to detect road intersections and their branch directions from an image. In the region of interest, it uses the contour shape of the segmented-intersection area to form a feature vector representing its geometric information. The extracted feature vector is then matched with some template vectors in order to find the best matched intersection pattern, obtain the type of intersection and the direction of connected roads. The experimental analysis are carried out with ISPRS Vaihingen and Toronto images. The experimental results show that the proposed method can extract most of the road intersections correctly. For the Vaihingen image, the the completeness and correctness are 81% and 87%, respectfully, while for the Toronto image, the the completeness and correctness are 78% and 85%, respectfully. It can help to build more correct and complete road network. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/113/2020/isprs-archives-XLIII-B3-2020-113-2020.pdf |
work_keys_str_mv |
AT pli automaticdetectionandrecognitionofroadintersectionsforroadextractionfromimagery AT yli automaticdetectionandrecognitionofroadintersectionsforroadextractionfromimagery AT jfeng automaticdetectionandrecognitionofroadintersectionsforroadextractionfromimagery AT zma automaticdetectionandrecognitionofroadintersectionsforroadextractionfromimagery AT xli automaticdetectionandrecognitionofroadintersectionsforroadextractionfromimagery |
_version_ |
1724642309556928512 |