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Unsupervised change detection using a novel fuzzy c-means clustering simultaneously incorporating local and global information

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Abstract

This paper presents a novel fuzzy c-means (FCM) clustering simultaneously incorporating local and global information (FLGICM) method to unsupervised change detection (CD) from remotely sensed images. A new factor including three local, global and edge parameters is added into the conventional FCM to enhance the insensitivity to noise and preserve detailed features. The spatial attraction between the central pixel and its neighborhood pixels is incorporated as a local parameter to utilize spatial information. A global parameter designed based on the estimated mean values of changed and unchanged pixels is introduced into the new factor to enhance its robustness and ability of separating changed from unchanged pixels. In addition, an edge parameter is also added to remain accurate edges and change details. Two experiments were carried out on Landsat images to test the performance of FLGICM. Experimental results indicate that FLGICM always achieves high accuracy and overperforms some state-of-the-art CD methods. Therefore, the proposed FLGIC provides an effective unsupervised CD method.

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References

  1. Bazi Y, Bruzzone L, Melgani F (2005) An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans Geosci Remote Sens 43(4):874–887

    Article  Google Scholar 

  2. Bazi Y, Melgani F, Al-Sharari HD (2010) Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Trans Geosci Remote Sens 48(8):3178–3187

    Article  Google Scholar 

  3. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, Norwell

    Book  MATH  Google Scholar 

  4. Bishop CM (2006) Pattern recognition and machine learning. Springer, Heidelberg

    MATH  Google Scholar 

  5. Bruzzone L, Cossu R (2002) A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps. IEEE Trans Geosci Remote Sens 40(9):1984–1996

    Article  MATH  Google Scholar 

  6. Bruzzone L, Prieto DF (2000) Automatic analysis of the difference image for unsupervised change detection. IEEE Trans Geosci Remote Sens 38(3):1171–1182

    Article  Google Scholar 

  7. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698

    Article  Google Scholar 

  8. Celik T, Ma KK (2011) Multitemporal image change detection using Undecimated discrete wavelet transform and active contours. IEEE Trans Geosci Remote Sens 49(2):706–716

    Article  Google Scholar 

  9. Chen J, Lu M, Chen X, Chen J, Chen L (2013) A spectral gradient difference based approach for land cover change detection. ISPRS J Photogramm Remote Sens 85:1–12

    Article  Google Scholar 

  10. Desclée B, Bogaert P, Defourny P (2006) Forest change detection by statistical object-based method. Remote Sens Environ 102(1):1–11

    Article  Google Scholar 

  11. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57

    Article  MathSciNet  MATH  Google Scholar 

  12. Ghosh A, Mishra NS, Ghosh S (2011) Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf Sci 181(4):699–715

    Article  Google Scholar 

  13. Gong M, Zhou Z, Ma J (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151

    Article  MathSciNet  Google Scholar 

  14. Gu W, Lv Z, Hao M (2015) Change detection method for remote sensing images based on an improved Markov random field. Multimed Tools Appl 1–16

  15. Hao M, Zhang H, Shi WZ, Deng KZ (2013) Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images. Remote Sens Lett 4(12):1185–1194

    Article  Google Scholar 

  16. Hao M, Shi WZ, Zhang H, Li C (2014) Unsupervised change detection with expectation-maximization-based level set. IEEE Geosci Remote Sens Lett 11(1):210–214

    Article  Google Scholar 

  17. Huang L, Wang M (1995) Image thresholding by minimizing the measures of fuzziness. Pattern Recogn 28(1):41–51

    Article  Google Scholar 

  18. Im JH, Jensen JR, Hodgson ME (2008) Optimizing the binary discriminant function in change detection applications. Remote Sens Environ 112(6):2761–2776

    Article  Google Scholar 

  19. Krinidis S, Chatzis V (2010) A robust fuzzy local information C-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337

    Article  MathSciNet  Google Scholar 

  20. Li H, Gong M, Liu J (2015) A local statistical fuzzy active contour model for change detection. IEEE Geosci Remote Sens Lett 12(3):582–586

    Article  Google Scholar 

  21. Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401

    Article  Google Scholar 

  22. Mishra NS, Ghosh S, Ghosh A (2012) Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images. Appl Soft Comput 12(8):2683–2692

    Article  Google Scholar 

  23. Moser G, Angiati E, Serpico SB (2011) Multiscale unsupervised change detection on optical images by Markov random fields and wavelets. IEEE Geosci Remote Sens Lett 8(4):725–729

    Article  Google Scholar 

  24. Nemmour H, Chibani Y (2006) Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS J Photogramm Remote Sens 61(2):125–133

    Article  Google Scholar 

  25. Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003

    Article  Google Scholar 

  26. Subudhi BN, Ghosh S, Nanda PK, Ghosh A (2016) Moving object detection using spatio-temporal multilayer compound markov random field and histogram thresholding based change detection. Multimed Tools Appl 1–33

  27. Wang Q, Shi W, Atkinson PM, Li Z (2015) Land cover change detection at subpixel resolution with a Hopfield neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(3):1339–1352

    Google Scholar 

  28. Xu D, Song R, Wu X, Li N, Feng W, Qian H (2014) Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts. Neurocomputing 143:144–152

    Article  Google Scholar 

  29. Xu D, Ricci E, Yan Y, Song J, Sebe N (2015) Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:151001553

  30. Ye Y, Shan J (2014) A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences. ISPRS J Photogramm Remote Sens 90:83–95

    Article  Google Scholar 

  31. Zheng N, Zhang H, Fan J, Guan H (2014) A fuzzy local neighbourhood-attraction-based information c-means clustering algorithm for very high spatial resolution imagery classification. Remote Sens Lett 5(9):843–852

    Article  Google Scholar 

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Acknowledgement

The work presented in this paper is supported by the Natural Science Foundation of Jiangsu Province under Grant BK20160248, the China Postdoctoral Science Foundation funded project, Fundamental Research Funds for the Central Universities under Grant 2015XKQY09, and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou, 221116, China

    Ming Hao, Zhang Hua & Zhenxuan Li

  2. School of Geography & Geomatics and Urban-Rural Planning, Jiangsu Normal University, Xuzhou, China

    Bingqian Chen

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  1. Ming Hao
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  2. Zhang Hua
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  3. Zhenxuan Li
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  4. Bingqian Chen
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Correspondence to Zhang Hua.

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Hao, M., Hua, Z., Li, Z. et al. Unsupervised change detection using a novel fuzzy c-means clustering simultaneously incorporating local and global information. Multimed Tools Appl 76, 20081–20098 (2017). https://doi.org/10.1007/s11042-017-4354-1

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  • DOI: https://doi.org/10.1007/s11042-017-4354-1

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