GEOMETRIC REFINEMENT OF ROAD NETWORKS USING NETWORK SNAKES AND SAR IMAGES

被引:3
|
作者
Butenuth, Matthias [1 ]
机构
[1] Tech Univ Munich, D-80333 Munich, Germany
来源
2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年
关键词
Network snakes; active contour model; refinement; roads; SAR image; EXTRACTION;
D O I
10.1109/IGARSS.2010.5652051
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
In this paper, a new approach for the geometric refinement of road networks using network snakes and SAR images is presented. Network snakes are based on the well-known active contour models, but in addition to the image energy and internal energy the topology is introduced into the optimization process. This graph-based active contour method enables a complete topological and shape control during the object delineation. The method is applied to the geometric refinement of road networks to improve and correct GIS-databases as a basis for traffic navigation or infrastructure planning purposes. The proposed approach is either able to deal with roads from a database as initialization in an automatic system or, alternatively, within an interactive framework to derive a geometrically optimized road network. The derived results using SAR images are evaluated with reference date to demonstrate the benefit and transferability of network snakes.
引用
收藏
页码:449 / 452
页数:4
相关论文
共 50 条
  • [21] A hierarchical receptive network oriented to target recognition in SAR images
    Dong, Ganggang
    Liu, Hongwei
    PATTERN RECOGNITION, 2022, 126
  • [22] Lane-Level Road Network Construction Based on Street-View Images
    Shi, Jinlin
    Li, Guannan
    Zhou, Liangchen
    Lu, Guonian
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 : 4744 - 4754
  • [23] Road and Car Extraction Using UAV Images via Efficient Dual Contextual Parsing Network
    Sun, Yueming
    Shao, Zhenfeng
    Cheng, Gui
    Huang, Xiao
    Wang, Zhongyuan
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [24] Offshore Bridge Detection in Polarimetric SAR Images Based on Water Network Construction Using Markov Tree
    Liu, Chun
    Yang, Jian
    Ou, Jianghong
    Fan, Dahua
    REMOTE SENSING, 2022, 14 (16)
  • [25] USING BUILDING AND BRIDGE INFORMATION FOR ADAPTING ROADS TO ALS DATA BY MEANS OF NETWORK SNAKES
    Goepfert, Jens
    Rottensteiner, Franz
    PCV 2010 - PHOTOGRAMMETRIC COMPUTER VISION AND IMAGE ANALYSIS, PT I, 2010, 38 : 163 - 168
  • [26] Dense Refinement Residual Network for Road Extraction From Aerial Imagery Data
    Eerapu, Karuna Kumari
    Ashwath, Balraj
    Lal, Shyam
    Dellacqua, Fabio
    Dhan, A. V. Narasimha
    IEEE ACCESS, 2019, 7 : 151764 - 151782
  • [27] Water flow based geometric active deformable model for road network
    Shanmugam, Leninisha
    Kaliaperumal, Vani
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2015, 102 : 140 - 147
  • [28] Combining Images and Trajectories Data to Automatically Generate Road Networks
    Bai, Xiangdong
    Feng, Xuyu
    Yin, Yuanyuan
    Yang, Mingchun
    Wang, Xingyao
    Yang, Xue
    REMOTE SENSING, 2023, 15 (13)
  • [29] RNGDet: Road Network Graph Detection by Transformer in Aerial Images
    Xu, Zhenhua
    Liu, Yuxuan
    Gan, Lu
    Sun, Yuxiang
    Wu, Xinyu
    Liu, Ming
    Wang, Lujia
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [30] Nearshore wave field analysis using SAR images
    Doong, DJ
    Kao, CC
    Chuang, ZS
    Lin, HP
    CHINA OCEAN ENGINEERING, 2003, 17 (01) : 45 - 60