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Journal of Electrical and Computer Engineering
Volume 2017 (2017), Article ID 3123967, 8 pages
https://doi.org/10.1155/2017/3123967
Research Article

Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images

Computer Science & Engineering Department, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

Correspondence should be addressed to Mumu Aktar

Received 11 March 2017; Revised 23 May 2017; Accepted 14 June 2017; Published 24 July 2017

Academic Editor: Huan Xie

Copyright © 2017 Mumu Aktar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Al Mamun, M. N. I. Mondal, and B. Ahmed, “Change detection-aided single linear prediction of multi-temporal satellite images,” in Proceedings of the 17th International Conference on Computer and Information Technology, ICCIT '14, pp. 332–335, IEEE, Dhaka, Bangladesh, December 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. P. Zhang, Z. Lv, and W. Shi, “Local spectrum-trend similarity approach for detecting land-cover change by using SPOT-5 satellite images,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 4, pp. 738–742, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. V. Alberga, “Similarity measures of remotely sensed multi-sensor images for change detection applications,” Remote Sensing, vol. 1, no. 3, pp. 122–143, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Tian, S. Cui, and P. Reinartz, “Building change detection based on satellite stereo imagery and digital surface models,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 406–417, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. D.-J. Kim, S. Hensley, S.-H. Yun, and M. Neumann, “Detection of durable and permanent changes in urban areas using multitemporal polarimetric UAVSAR data,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 2, pp. 267–271, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Liu, L. Bruzzone, F. Bovolo, and P. Du, “Unsupervised hierarchical spectral analysis for change detection in hyperspectral images,” in Proceedings of the 2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS '12, IEEE, Shanghai, China, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Moser, S. Voigt, E. Schoepfer, and S. Palmer, “Multitemporal wetland monitoring in sub-Saharan West-Africa using medium resolution optical satellite data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 8, pp. 3402–3415, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. P. R. Coppin and M. E. Bauer, “Digital change detection in forest ecosystems with remote sensing imagery,” Remote Sensing Reviews, vol. 13, no. 3-4, pp. 207–234, 1996. View at Publisher · View at Google Scholar · View at Scopus
  9. N. A. Quarmby and J. L. Cushnie, “Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in south-east England,” International Journal of Remote Sensing, vol. 10, no. 6, pp. 953–963, 1989. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Bazi, L. Bruzzone, and F. Melgani, “An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 874–886, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Inglacla and G. Mercier, “A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432–1445, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. A. K. Ludeke, R. C. Maggio, and L. M. Reid, “An analysis of anthropogenic deforestation using logistic regression and GIS,” Journal of Environmental Management, vol. 31, no. 3, pp. 247–259, 1990. View at Publisher · View at Google Scholar · View at Scopus
  13. E. H. Wilson and S. A. Sader, “Detection of forest harvest type using multiple dates of landsat TM imagery,” Remote Sensing of Environment, vol. 80, no. 3, pp. 385–396, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. M.-L. Nordberg and J. Evertson, “Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM and ETM+imagery,” Land Degradation and Development, vol. 16, no. 2, pp. 139–149, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. J. S. Deng, K. Wang, Y. H. Deng, and G. J. Qi, “PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data,” International Journal of Remote Sensing, vol. 29, no. 16, pp. 4823–4838, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. N. Otsu, “A threshold selection method from gray-level histograms,” Automatica, vol. 11, pp. 23–27, 1975. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Li, X. Li, Y. Zhang, L. Wang, and G. Ying, “Change detection for high-resolution remote sensing imagery using object-oriented change vector analysis method,” in Proceedings of the 36th International Geoscience and Remote Sensing Symposium, IGARSS '16, pp. 2873–2876, IEEE, Beijing, China, July 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. K. G. Pillai and R. R. Vatsavai, “Multi-sensor remote sensing image change detection: an evaluation of similarity measures,” in Proceedings of the 2013 13th International Conference on Data Mining Workshops, ICDMW '13, pp. 1053–1060, IEEE, Dallas, Tex, USA, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. M. A. Hossain, X. Jia, and M. Pickering, “Subspace detection using a mutual information measure for hyperspectral image classification,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 2, pp. 424–428, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Prendes, M. Chabert, F. Pascal, A. Giros, and J.-Y. Tourneret, “A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors,” IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 799–812, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. S. Cui, G. Schwarz, and M. Datcu, “A benchmark evaluation of similarity measures for multitemporal SAR image change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 3, pp. 1101–1118, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. A. A. Nielsen and J. S. Vestergaard, “Change detection in bi-temporal data by canonical information analysis,” in Proceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Multi-Temp '15, IEEE, Annecy, France, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. L. An, M. Li, P. Zhang, Y. Wu, L. Jia, and W. Song, “Multicontextual mutual information data for SAR image change detection,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 9, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. E. Erten, A. Reigber, L. Ferro-Famil, and O. Hellwich, “A new coherent similarity measure for temporal multichannel scene characterization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 7, pp. 2839–2851, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. T. M. Cover and J. A. Thomas, Elements of Information Theory, John Wiley & Sons, Hoboken, NJ, USA, 2nd edition, 2012. View at MathSciNet
  26. S. Liu, L. Bruzzone, F. Bovolo, and P. Du, “Hierarchical unsupervised change detection in multitemporal hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 244–260, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. M. İlsever and C. Ünsalan, Two-Dimensional Change Detection Methods, Springer Briefs in Computer Science, 2012. View at Publisher · View at Google Scholar