A comparison of road network extraction from High Resolution Images

Study related to high resolution remote sensing (RS) images has been applicable to many areas that are beneficial to us for information related to territory, public, weather and agriculture, the main aim of RS applications is extract appropriate information that will help us to draw meaningful conclusions. Road network extraction is an important and challenging research field, road networks are essential for humans as they provide transportation and other support systems. Distinct factors like sensor, weather, resolution and light etc., can affect the road features from a RS image which imposes the problems in road network extraction. This paper presents a comprehensive analysis of various aspects of road network extraction from RS images like road features, problems in road network extraction and finally different road network methods are classified on the basis of local and global features, automation and algorithm used. Different road extraction techniques are compared on the basis of features used in extraction, number and type of data (aerial, hyper-spectral, remote sensing, urban and semi-urban) used, performance on the basis of different qualitative parameters and the advantage and disadvantages of different methods are discussed. The comparative analysis of road extraction methods is presented emphatically and it is observable that in order to obtain precise road network from RS images only one type of feature is not sufficient. Hence, multiple road features should be combined together but it depends on type of application and data. Road network extraction from RS image is still remains a challenging and important research field.

[1]  C. Steger,et al.  AUTOMATIC ROAD EXTRACTION BASED ON MULTI-SCALE, GROUPING, AND CONTEXT , 1999 .

[2]  Zulin Wang,et al.  Road Structure Refined CNN for Road Extraction in Aerial Image , 2017, IEEE Geoscience and Remote Sensing Letters.

[3]  Keiichi Uchimura,et al.  Urban road extraction based on hough transform and region growing , 2013, The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision.

[4]  Haihong Li,et al.  Road extraction from aerial and satellite images by dynamic programming , 1995 .

[5]  Chinnathevar Sujatha,et al.  Connected component-based technique for automatic extraction of road centerline in high resolution satellite images , 2015, EURASIP Journal on Image and Video Processing.

[7]  E. Baltsavias,et al.  Road network detection by mathematical morphology , 1999 .

[8]  Emmanuel Christophe,et al.  Robust Road Extraction for High Resolution Satellite Images , 2007, 2007 IEEE International Conference on Image Processing.

[9]  Juan B. Mena,et al.  State of the art on automatic road extraction for GIS update: a novel classification , 2003, Pattern Recognit. Lett..

[10]  A. Gruen,et al.  Semi-Automatic Linear Feature Extraction by Dynamic Programming and LSB-Snakes , 1997 .

[11]  Sukhendu Das,et al.  Use of Salient Features for the Design of a Multistage Framework to Extract Roads From High-Resolution Multispectral Satellite Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[12]  A. Mohammadzadeh,et al.  Road extraction based on fuzzy logic and mathematical morphology from pan‐sharpened ikonos images , 2006 .

[13]  David B. Cooper,et al.  Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Richard Lepage,et al.  Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet Transform , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Jianhua Wang,et al.  A New Approach to Urban Road Extraction Using High-Resolution Aerial Image , 2016, ISPRS Int. J. Geo Inf..

[16]  A. Mookambiga,et al.  Automated road network extraction using artificial neural network , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[17]  Claudionor Ribeiro da Silva,et al.  Automatic road extraction in rural areas, based on the Radon transform using digital images , 2010 .

[18]  Mojgan Pashaie Nejad,et al.  Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images , 2018, Journal of the Indian Society of Remote Sensing.

[19]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[20]  Wenzhong Shi,et al.  An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Mrinal K. Ghose,et al.  Land Cover Classification of Remotely Sensed Satellite Data using Bayesian and Hybrid classifier , 2010 .

[22]  Hai Jin,et al.  Image Thresholding Using Graph Cuts , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  Rahul Dev Garg,et al.  Automatic Road Extraction from High Resolution Satellite Image using Adaptive Global Thresholding and Morphological Operations , 2013, Journal of the Indian Society of Remote Sensing.

[24]  Jean-Baptiste Mouret,et al.  Fast Road Network Extraction in Satellite Images Using Mathematical Morphology and Markov Random Fields , 2004, EURASIP J. Adv. Signal Process..

[25]  M. Sharmila,et al.  Automatic road extraction using high resolution satellite images based on level set and mean shift methods , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[26]  M. Özkaya,et al.  ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES , 2012 .

[27]  José A. Malpica,et al.  An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery , 2005, Pattern Recognit. Lett..

[28]  Ying Wang,et al.  Urban road extraction via graph cuts based probability propagation , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[29]  Xiang Wen,et al.  Road Extraction Based on the Algorithms of MRF and Hybrid Model of SVM and FCM , 2011, 2011 International Symposium on Image and Data Fusion.

[30]  Zhengjun Liu,et al.  Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation , 2009, Sensors.

[31]  Jiao Jiao,et al.  An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network , 2018, IEEE Access.

[32]  Shiming Xiang,et al.  Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Qiming Qin,et al.  Road extraction from ETM panchromatic image based on Dual-Edge Following , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[34]  L. Pratap Reddy,et al.  Automatic Road Extraction based on Level Set,Normalized Cuts and Mean Shift Methods , 2011 .

[35]  S. Natarajan,et al.  Road Extraction Using Topological Derivative and Mathematical Morphology , 2013, Journal of the Indian Society of Remote Sensing.

[36]  Shi Qixin,et al.  A methodology for Automatic Detection and Extraction of Road Edges from High Resolution Remote Sensing Images , 2006, 2006 IEEE International Conference on Industrial Technology.

[37]  Peter Wonka,et al.  Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints , 2007, IEEE Transactions on Geoscience and Remote Sensing.