A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Karmuhil, P.
- An Automatic Road Network Extraction from Satellite Images Using Modified SOFM Approach
Authors
1 Research Scholar, Periyar University, GSS Jain College for Women, Chennai, IN
2 Research Supervisor, Periyar University, Pondicherry University Community college, Pondicherry, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 4 (2016), Pagination: 1-6Abstract
Objective: The objective of this study is an Automatic Extraction of road networks from very high resolution satellite images. It is an important research area in remote sensing field.
Methods/Analysis: We present fully automatic road extraction from high resolution satellite images using modified self organizing map. Firstly, it focuses a road detection using self organizing map algorithm. At the end, the T-Cluster method is used to improve the segmentation in road networks.
Findings: Experimental results show the significant accuracy (90%) and efficiency of proposed approach.
Application/Improvement: The modified T-SOM technique provides the resources for readily creating, maintaining and updating the road transportation databases used in vehicle tracking and traffic management.
Keywords
Road Extraction, Remote Sensing Field, Self Organizing Map, T-Cluster, T-SOM.References
- A. Grote, C.Heipke. Road Extractionfor the update of road databases in sub urban areas. The International Archives of the photogrammetry, Remote Sensng and Spatial information Sciences.2008; XXXVII, Part B2b, 563568.
- L. Xu, T. Jun, Y. Xiang, C. Jianjie. The rapid method for road extraction from high resolution satellite images based on usm algorithm. International conference on Image analysis and signal Processing, Hangzhou, China, November, 2012.
- D. Chaudhuri, N. Kushwaha, A. KsSamal. Semi-Automated Road detection from High resolution Satellite images by Directional Morphological Enhancement and Segmentation Techniques. IEEE Journal of selected Topics in Applied Earth observations and Remote Sensing. 2012; 5(5), 1538-1544.
- A Lizy, M. Sasikumar. A Fuzzy based Road network Extraction from Degraded satellite images. IEEE, International Conference on Advances in computing Communications and Informatics (ICACCI). 2013; 2032-2036.
- Z. Shu, D. Wang, C. Zhou. Road Geometric Features Extraction based on Self organizing map(SOM) Neural network. Journal of Networks. 2014; 9(1), 190-197 .
- S. Jenitha. Hybrid Heuristic-Based Artificial Immune System for Task Scheduling. International journal of Distributed and Parallel Systems(IJDPS). 2011 November; 2(6), 1-12.
- T. Ranjani Mangala, S. G. Bhirud. A New Automatic Road Extraction Technique using gradient operation and Skeletal Ray Formation.International Journal of Computer Applications, 2011 September; 29(1), 17-25.
- K. Shahi, Z. M. Helmi, Z. M. Shafri, E. Taherzadeh, S. Mansor, R. Muniandy. A Novel spectral index to automatically extract road networks from World View-2 satellite imagery. Elsevier, The Egyptian Journal of Remote Sensing and Space Sciences. 2015.
- S. Leninisha, K. Vani. Water flow based geometric active deformable model for road network. ISPRS Journal of Photogrammetry and Remote Sensing. 2015; 102,140-147.
- T. Onur. Fully Automatic Road Network Extraction from Satellite Images. IEEE, 2007; 708-714.
- T. Kohonen. Self-Organizing Maps. (2nd edn), Springer-verlag. 1997.
- P.Y. Shinzato, D.F. Wolf. A Road network following approach using Artificial Neural Networks combinations. Journal of Intelligent & Robotic systems. 2011 June; 62(4), 527-546.
- J. Deng, J. Hu, J. Wu. A study of color space transformation method using nonuniform segmentation of color space source.Journal of computers.2011; 6(2),288-296.
- K. P. Kaliya murthie, D. Parameswari.Remote Sensing imaging for Satellite Image Segmentation.Indian Journal of Science and Technology. 2015; 8(31), 1-5.
- C. Simon, K. Peter, R. Rranz.The Automatic Extraction of Roads from Lidar data. ISPRS, Istanbul, Turkey, 2004,July, 12-23.