Open Access
MATEC Web Conf.
Volume 120, 2017
International Conference on Advances in Sustainable Construction Materials & Civil Engineering Systems (ASCMCES-17)
Article Number 09006
Number of page(s) 12
Section Geographic Information Systems & Remote Sensing
Published online 09 August 2017
  1. A. K. Nassar, G. Alan Blackburn, and J. Duncan Whyatt, “Developing the desert: The pace and process of urban growth in Dubai,” Computers, Environment and Urban Systems, vol. 45, pp. 50–62, (2014). [CrossRef] [Google Scholar]
  2. H. S. Sudhira, T. V. Ramachandra, and K. S. Jagadish, “Urban sprawl: Metrics, dynamics and modelling using GIS,” International Journal of Applied Earth Observation and Geoinformation, vol. 5, no. 1, pp. 29–39, (2004). [CrossRef] [Google Scholar]
  3. W. Ji, Y. Wang, D. Zhuang, D. Song, X. Shen, W. Wang, and G. Li, “Spatial and temporal distribution of expressway and its relationships to land cover and population: A case study of Beijing, China,” Transportation Research Part D: Transport and Environment, vol. 32, pp. 86–96, (2014). [CrossRef] [Google Scholar]
  4. [M. K. Jat, P. K. Garg, and D. Khare, “Monitoring and modelling of urban sprawl using remote sensing and GIS techniques,” International Journal of Applied Earth Observation and Geoinformation, vol. 10, no. 1, pp. 26–43, (2008). [CrossRef] [Google Scholar]
  5. A. a a Al-sharif, B. Pradhan, H. Zulhaidi, M. Shafri, and S. Mansor, “Spatio-temporal Analysis of Urban and Population Growths in Tripoli using Remotely Sensed Data and GIS,” Indian Journal of Science and technology, vol. 6, no. 8, pp. 5134–5142, (2013). [Google Scholar]
  6. A. Singh, “Review article digital change detection techniques using remotely-sensed data,” International journal of remote sensing, vol. 10, no. 6, pp. 989–1003,( 1989). [CrossRef] [Google Scholar]
  7. I. Dowman, “Automated procedures for integration of satellite images and map data for change detection: the archangel project,” International Archives of Photogrammetry and Remote Sensing, vol. 32, pp. 162–169, (1998). [Google Scholar]
  8. R. I. Al-Ruzouq and A. F. Habib, “Linear features for automatic registration and reliable change detection of multi-source imagery,” Journal of Spatial Science, vol. 57, no. 1, pp. 51–64,( 2012). [CrossRef] [Google Scholar]
  9. L. Bruzzone and D. F. Prieto, “Automatic analysis of the difference image for unsupervised change detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 3, pp. 1171–1182, (2000). [Google Scholar]
  10. MathWorks, “Edge Detection.” [Online]. Available: [Accessed: 03-Aug-2016]. [Google Scholar]
  11. X. J. Yu and C. N. Ng, “Spatial and temporal dynamics of urban sprawl along two urban-rural transects: A case study of Guangzhou, China,” Landscape and Urban Planning, vol. 79, no. 1, pp. 96–109, (2007). [CrossRef] [Google Scholar]
  12. M. Aljoufie, M. Zuidgeest, M. Brussel, and M. van Maarseveen, “Spatial-temporal analysis of urban growth and transportation in Jeddah City, Saudi Arabia,” Cities, vol. 31, pp. 57–68, (2013). [CrossRef] [Google Scholar]
  13. T. Blaschke, “Object based image analysis for remote sensing,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 1, pp. 2–16, (2010). [Google Scholar]
  14. R. C. Weih and N. D. Riggan, “Object-based classification vs. pixel-based classification: Comparitive importance of multi-resolution imagery,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVIII, pp. 1–6, (2010). [Google Scholar]
  15. J. Norton, D. Gallant, and N. Scotia, “Supervised Image Classification,” no. March, (2014). [Google Scholar]
  16. G. M. Foody and A. Mathur, “Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification,” Remote Sensing of Environment, vol. 93, no. 1–2, pp. 107–117, (2004). [CrossRef] [Google Scholar]
  17. T. W. Lee and M. S. Lewicki, “Unsupervised image classification, segmentation, and enhancement using ICA mixture models,” IEEE Transactions on Image Processing, vol. 11, no. 3, pp. 270–279, (2002). [CrossRef] [Google Scholar]
  18. A. E. M. Omran A. Salman, “Differential evolution methods for unsupervised image classification,” Proc. of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 966–973, (2005). [Google Scholar]
  19. Dubai Statistical Center, “Population-Clock @” [Online]. Available: [Google Scholar]
  20. Archived from, “background @” [Online]. Available: [Google Scholar]

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