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Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering


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1. Gong, M., Z. Zhou, J. Ma. Change Detection in Synthetic Aperture Radar Images Basedon Image Fusion and Fuzzy Clustering. – IEEE Transactions on Image Processing, Vol. 21, 2012, No 4, pp. 2141-2151.10.1109/TIP.2011.217070221984509 Search in Google Scholar

2. Jakka, T. K., Y. Mallikarjuna Reddy, B. Prabhakara Rao. GWDWT-FCM: Change Detection in SAR Images Using Adaptive Discrete Wavelet Transform with Fuzzy c-Mean Clustering. – Journal of the Indian Society of Remote Sensing, Vol. 47, 2019, No 3, pp. 379-390.10.1007/s12524-018-0901-0 Search in Google Scholar

3. Kumar, J. T., Y. M. Reddy, B. P. Rao. Image Fusion of Remote Sensing Images Using ADWT with ABC Optimization Algorithm. – International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 8, 2019, Issue 11. ISSN: 2278-3075.10.35940/ijitee.K2309.0981119 Search in Google Scholar

4. Kumar, J. T., Y. M. Reddy, B. P. Rao. Change Detection in Sarimages Based on Artificial Bee Colony Optimization with Fuzzy c-Means Clustering. – International Journal of Recent Technology and Engineering (IJRTE), Vol. 7, 2018, Issue 4. ISSN: 2277-3878. Search in Google Scholar

5. Kumar, J. T., Y. Mallikarjuna Reddy, B. Prabhakara Rao. WHDA-FCM: Wolf Hunting-Based Dragonfly with Fuzzy c-Mean Clustering for Change Detection in SAR Images. – The Computer Journal, Section B: Computer and Communications Networks and Systems, Vol. 63, February 2020, Issue 2, pp. 308-321.10.1093/comjnl/bxz130 Search in Google Scholar

6. Inglada, J., G. Mercier. A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis. – IEEE Trans. Geosci. Remote Sens., Vol. 45, 2017, No 5, pp. 1432-144510.1109/TGRS.2007.893568 Search in Google Scholar

7. Singh, A. Digital Change Detection Techniques Using Remotely Sensed Data. – International Remote Sensing, Vol. 10, 1989, No 6, pp. 989-1003.10.1080/01431168908903939 Search in Google Scholar

8. Rignot, E. J. M., J. J. Van Zyl. Change Detection Techniques for ERS-1 SAR Data. – IEEE Trans. Geosci. Remote Sens., Vol. 31, 1993, No 4, pp. 896-906.10.1109/36.239913 Search in Google Scholar

9. Mirjalili1, S. Dragonfly Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-Objective, Discrete, and Multi-Objective Problems. – Neural Comput&Applic, Vol. 27, 2016, pp. 1053-1073.10.1007/s00521-015-1920-1 Search in Google Scholar

10. SreeRanjini, K. S., S. Murugan. Memory-Based Hybrid Dragonfly Algorithm for Numerical Optimization Problems. – Expert Systems with Applications, Vol. 83, 2017, pp. 63-78.10.1016/j.eswa.2017.04.033 Search in Google Scholar

11. Çigdeminan, A., H. Gulkan. A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion. – Hindawi Computational Intelligence and Neuroscience, 2019. Article ID 6871298. 17 p.10.1155/2019/6871298658931031281336 Search in Google Scholar

12. Vrionis, T. D., X. I. Koutiva, N. A. Vovos. A Genetic Algorithm-Based Low Voltage Ride-Through Control Strategy for Grid Connected Doubly Fed Induction Wind Generators. – IEEE Transactions on Power Systems, Vol. 29, 2014, No 3, pp. 1325-1334.10.1109/TPWRS.2013.2290622 Search in Google Scholar

13. Hui, Z., Y. Fei. A Novel Fuzzy Clustering Recommendation Algorithm Based on PSO. – Cybernetics and Information Technologies, Vol. 14, 2014, No 1, pp. 108-117.10.2478/cait-2014-0048 Search in Google Scholar

14. Zhang, J., P. Xia. An Optimized Scheduling Algorithm on a Cloud Workflow Using a Discrete Particle Swarm. –Cybernetics and Information Technologies, Vol. 14, 2014, No 1, pp. 25-39.10.2478/cait-2014-0003 Search in Google Scholar

15. Yan, W., S. Shi, L. Pan, G. Zhang. Unsupervised Change Detection in SAR Images Based on Frequency Difference and a Modified Fuzzy c-Means Clustering. – International Journal of Remote Sensing, Vol. 39, 2018, No 10, pp. 3055-3075.10.1080/01431161.2018.1434325 Search in Google Scholar

16. Qiu, F., J. Berglund, J. R. Jensen, P. Thakkar, D. Ren. Speckle Noise Reduction in SAR Imagery Using a Local Adaptive Median Filter. –GIScience and Remote Sensing, Vol. 3, 2004, pp. 244-266.10.2747/1548-1603.41.3.244 Search in Google Scholar

17. Zhuang, H., Z. Tan, K. Deng, H. Fan. It is a Misunderstanding that Log-Ratio Outperforms Ratio in Change Detection of SAR Images. – European Journal of Remote Sensing, Vol. 52, 2019, No 1, pp. 484-492.10.1080/22797254.2019.1653226 Search in Google Scholar

18. Vijaya Geetha, R., S. Kalaivani. Laplacian Pyramid-Based Change Detection in Multitemporal SAR Images. – European Journal of Remote Sensing, Vol. 5, 2019.10.1080/22797254.2019.1640077 Search in Google Scholar

19. Liu, T., C. Q. Guo, Y. Yuan, W. Li, Q. Yan. An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification. – Energies, Vol. 11, 2018, pp. 1-15.10.3390/en11010095 Search in Google Scholar

20. Vrionis, T., X. Koutiva, Nicholas. A Genetic Algorithm-Based Low Voltage Ride-Through Control Strategy for Grid Connected Doubly Fed Induction Wind Generators. – IEEE Transactions on Power Systems, 2014, Vol. 29, No 3, pp. 1325-1334.10.1109/TPWRS.2013.2290622 Search in Google Scholar

21. Mirjalili, S. The Ant Lion Optimizer. – Adv. Eng. Software, Vol. 83, 2015, pp. 80-98.10.1016/j.advengsoft.2015.01.010 Search in Google Scholar

22. Zhao, M., Q. Ling, F. Li. An Iterative Feedback-Based Change Detection Algorithm for Flood Mapping in SAR Images. – IEEE Geoscience and Remote Sensing Letters, Vol. 16, February 2019, No 2, pp. 231-235.10.1109/LGRS.2018.2871849 Search in Google Scholar

23. Li, H., Q. Zhao, G. Yang, K. Fu, W. J. Emery. Robust Semi-NMF with Total Variation for Unsupervised SAR Image Change Detection. – Electronics Letters, Vol. 54, 12.07.2018, No 14, pp. 892-894. Search in Google Scholar

24. Lazarov, A., C. Minchev. ISAR Image Recognition Algorithm and Neural Network Implementation. – Cybernetics and Information Technologies, Vol. 17, 2017, No 4, pp. 183-199.10.1515/cait-2017-0048 Search in Google Scholar

25. Hou, B., Q. Wei, Y. Zheng, S. Wang. Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection. – IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, August 2014, No 8, pp. 3297-3317.10.1109/JSTARS.2014.2328344 Search in Google Scholar

26. Zheng, Y., X. Zhang, B. Hou, G. Liu. Using Combined Difference Image and $k$-Means Clustering for SAR Image Change Detection. – IEEE Geoscience and Remote Sensing Letters, Vol. 11, March 2014, No 3, pp. 691-695.10.1109/LGRS.2013.2275738 Search in Google Scholar

27. Zhang, X., J. Chen, H. Meng. A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model. – IEEE Geoscience and Remote Sensing Letters, Vol. 10, January 2013, No 1, pp. 14-18.10.1109/LGRS.2012.2189867 Search in Google Scholar

28. Gong, M., Z. Zhou, J. Ma. Change Detection in Synthetic Aperture Radar Images Based on Image Fusion and Fuzzy Clustering. – IEEE Transactions on Image Processing, Vol. 21, April 2012, No 4, pp. 2141-2151.10.1109/TIP.2011.217070221984509 Search in Google Scholar

29. Moser, G., S. B. Serpico. Unsupervised Change Detection From Multichannel SAR Data by Markovian Data Fusion. – IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, July 2009, No 7, pp. 2114-2128.10.1109/TGRS.2009.2012407 Search in Google Scholar

eISSN:
1314-4081
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Computer Sciences, Information Technology