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Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.
Rocznik
Strony
403--408
Opis fizyczny
Bibliogr. 24 poz., fot., schem., tab., wykr.
Twórcy
  • University of Diyala, College of Engineering, Dept. of Communication Engineering
  • University of Diyala, College of Engineering, Dept. of Communication Engineering
Bibliografia
  • [1] Feng Gao, Junyu Dong, Bo Li, Qizhi Xu, Cui Xie, “Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine,” J. Appl. Remote Sens. 10(4), 046019 (2016), doi: 10.1117/1.JRS.10.046019.
  • [2] Turgay Celik, "Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering", IEEE geoscience and remote sensing letters, vol. 6, no. 4, October 2009.
  • [3] Xinzheng Zhang, Guo Liu, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xin Jian, Xichuan Zhou and Yongming Li, "Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection", Remote Sensing. 2020.
  • [4] Karpenko A.P., Seliverstov E.Yu. Review of the particle swarm optimization method (PSO) for a global optimization problem. Nauka i obrazovanie. MGTU im. N.E. Baumana [Science and Education of the Bauman MSTU], 2009, no. 3 (in Russ.). DOI: 10.7463/00309.0116072.
  • [5] Xinzheng Zhang, Hang Su, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xiaoping Zeng and Xin Jian. "A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet", arXiv.org > cs > arXiv:2003.01768, 2020
  • [6] Feng Gao, Xiao Wang, Junyu Dong, Shengke Wang, "SAR Image Change Detection Based on Frequency Domain Analysis and Random Multi-Graphs", Journal of Applied Remote Sensing, 2017
  • [7] Feng Gao, Junyu Dong, Bo Li, and Qizhi Xu, "Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet", IEEE geoscience and remote sensing letters, vol. 13, no. 12, 2016.
  • [8] Li Yufeng & He Wei, "Research on SAR image change detection algorithm based on hybrid genetic FCM and image registration", Springer Science+Business Media New York 2017.
  • [9] Yunhao Gao, Feng Gao, Junyu Dong, and Shengke Wang, "Change Detection from Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network", IEEE journal of selected topics in applied earth observations and remote sensing, 2019.
  • [10] Wenping Ma, Hui Yang, Yue Wu, Yunta Xiong, Tao Hu, Licheng Jiao and Biao Hou, "Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images", Remote Sensing. 2019.
  • [11] Jun Wang, Xuezhi Yang, Xiangyu Yang, Lu Jia, Shuai Fang, "Unsupervised change detection between SAR images based on hypergraphs", ISPRS Journal of Photogrammetry and Remote Sensing 164 (2020) 61–72
  • [12] J Kennedy, R Eberhart. Particle swarm optimization. // Proceedings of IEEE International conference on Neural Networks. – 1995, pp. 1942-1948.
  • [13] Rupak Chakraborty, Rama Sushil, M. L. Garg, "An Improved PSO-Based Multilevel Image Segmentation Technique Using Minimum Cross-Entropy Thresholding", Arabian Journal for Science and Engineering, King Fahd University of Petroleum & Minerals 2018.
  • [14] Nameirakpam Dhanachandra, Yambem Jina Chanu, "An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm", Springer Science+Business Media, LLC, part of Springer Nature 2020.
  • [15] Jin Liu, Zilu Wu, Qi Li " A Novel Local Feature Extraction Algorithm Based on Gabor Wavelet Transform", ICAIP 2019: Proceedings of the 2019 3rd International Conference on Advances in Image Processing.
  • [16] David Bařina, “Gabor Wavelets in Image Processing”, Proceedings of conference and competitions student EEICT 2011, Czech Republic, pp. 1-5.
  • [17] Deepak Verma, Dr. Vijaypal Dhaka, Shubhlakshmi Agrwa, “An Improved Average Gabor Wavelet Filter Feature Extraction Technique for Facial Expression Recognition”, International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2 Issue 4 August 2013, pp. 35-41.
  • [18] Youguo Li, Haiyan Wu, "A Clustering Method Based on K-Means Algorithm", 2012 International Conference on Solid State Devices and Materials Science
  • [19] Joaquín Pérez-Ortega, Nelva Nely Almanza-Ortega, Andrea Vega-Villalobos, Rodolfo Pazos-Rangel, Crispín Zavala-Díaz and Alicia Martínez-Rebollar, "The K-Means Algorithm Evolution", book, April 3rd 2019, DOI: 10.5772/intechopen.85447
  • [20] T. Celik, “Unsupervised change detection in satellite images using principal component analysis and k-means clustering,” IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 772–776, Oct. 2009.
  • [21] F. Gao, X. Wang, Y. Gao, J. Dong, and S. Wang, “Sea ice change detection in SAR images based on convolutional-wavelet neural networks” IEEE Geosci. Remote Sens. Lett., vol. 16, no. 8, pp. 1240–1244, Aug. 2019.
  • [22] Maoguo Gong, Meng Jia, Linzhi Su, Shuang Wang & Licheng Jiao, "Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts" Journal International Journal of Remote Sensing, 2014, Remote sensing of the China seas
  • [23] Stelios Krinidis ; Vassilios Chatzis, "A Robust Fuzzy Local Information C-Means Clustering Algorithm", IEEE Transactions on Image Processing , May 2010.
  • [24] Maoguo Gong, Linzhi Su, Meng Jia, Weisheng Chen, "Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images", IEEE Transactions on Fuzzy Systems, Feb. 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-efe36fbe-da57-4263-9781-7cefdc797cee
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