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Change detection of remote sensing images through DT-CWT and MRF

  • role： First author第一作者
  • Affiliation：

    School of Environmental Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China

  • Email：fkk@cumt.edu.cn
  • Introduction：范奎奎(1991—　)，男，硕士研究生，研究方向为遥感影像变化检测、小波变换图像处理。E-mail：fkk@cumt.edu.cn
FAN Kuikui,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Environmental Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China

  • Email：wzy95002@163.com
  • Introduction：王中元(1977—　)，男，副教授，研究方向为3S集成及其应用、大比例尺地形图更新。E-mail：wzy95002@163.com
WANG Zhongyuan1*,  
  • Affiliation：

    School of Environmental Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China

OUYANG Sida2,  
  • Affiliation：

    School of Environmental Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China

WANG Huibing2,  
  • Affiliation：

    School of Environmental Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China

SHI Shaoyu2

Resümee

Image change detection uses an algorithm that is based on multiscale analysis and Markov Random Field (MRF) model. The algorithm is widely used owing to time–frequency wavelet characteristics and the good expression to spatial correlation of the MRF model. To address the significant loss of high-frequency information during noise reduction and pixel independence in change detection of multiscale remote sensing images, this paper proposes an unsupervised change detection algorithm based on the combined dual-tree complex wavelet transform (DT-CWT) and MRF. The algorithm can be divided into the following steps. First, to enhance detail expression and objective image edges, the difference image is decomposed on a multiscale by DT-CWT. Second, the change characteristics in high-frequency regions are extracted by setting the high-frequency components of the first layer to zero and performing MRF segmentation to the other levels. Third, the high- and low-frequency sub-bands of each layer are reconstructed, and the maximum a posteriori probability is estimated by the Iterative Condition Model (ICM) based on the K-means segmentation algorithm, which fully obtains the correlation between pixels. Finally, the segmentation results of each layer are fused to obtain the mask of the final change detection result. To verify the effectiveness and stability of the proposed algorithm, the DT-CWT–Bayes, MRF–Bayes, DWT-MRF–Bayes methods, and the proposed algorithm are comparatively tested and analyzed. The contrast experiment proves that compared with the other methods, the proposed method produces change detection results for edges that subjectively look smoother and more delicate with less noise. In addition, as shown in the table of evaluation indices of the four change detection methods, the proposed method has the least total number of errors and the highest accuracy rate. Thus, the proposed method balances the reduction of tiny spots and noise and the retention of high frequency information. Moreover, the proposed method has high precision for change detection and predominant robust properties. The proposed algorithm fully uses the multidirectional expression, anisotropy, and multiscale properties of DT-CWT, which helps the expression and analysis of image information. In addition, the extraction of the change characteristics in high-frequency regions based on the segmentation algorithm of ICM better balances between noise reduction and retention of high frequency information. Finally, the final iteration and segmentation based on the MRF segmentation algorithm determines the correlation between pixels with considerably reduced false alarm rates while avoiding the influence of registration error. However, the proposed algorithm takes more time due to the existence of some iterative processes.

Schlüsselwort

change detection;DT-CWT;MRF;multi-scale decomposition

References

  1. 1.
    Bruzzone L and Prieto D F. 2000.Automatic analysis of the difference image for unsupervised change detection.IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171–1182
  2. 2.
    Celik T and Ma KK. 2010. Unsupervised change detection for satellite images using dual-tree complex wavelet transform. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1199–1210
  3. 3.
    Chen Z H, Wang W X, Yu T C and Lin L Q.2011.Remote sensing image variation detection on multi-scale MRF fusion.Journal of Sichuan University(Engineering Science Edition), 43(3): 104–108
  4. 4.
    Dian Y Y, Fang S H and Yao C H. 2014.The geographic object-based method for change detection with remote sensing imagery.Geomatics and Information Science of Wuhan University, 39(8): 906–912
  5. 5.
    Dian Y Y, Fang S H and Yao C H. 2016.Change detection for high-resolution images using multilevel segment method. Journal of Remote Sensing, 20(1): 129–137
  6. 6.
    Geman S and Geman D. 1984.Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images.IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(6): 721–741
  7. 7.
    Kasetkasem T and Varshney P K. 2002. An image change detection algorithm based on Markov random field models. IEEE Transactions on Geoscience and Remote Sensing, 48(8): 1815–1823
  8. 8.
    Kingsbury N. 1998. The dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement//Proceedings of the9th European Signal Processing Conference. Rhodes: IEEE, 319–322.
  9. 9.
    Kingsbury N. 2001. Complex wavelets for shift invariant analysis and filtering of signals.Applied and Computational Harmonic Analysis, 10(3): 234–253
  10. 10.
    Li C C, Qi X D and Zhang G S. 2013.A study on SAR image change detection based on Markov Random Field models. Modern Radar,35(7): 31–35
  11. 11.
    Li C C, Zhang G S, Li H D and Li D D. 2014.A study on SAR image change detection based on improved Markov random field models. Modern Radar, 36(9): 36–39
  12. 12.
    Marchesi S and Bruzzone L. 2009. ICA and kernel ICA for change detection in multispectral remote sensing images//Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town: IEEE, 2: II-980-II-983 [DOI: 10.1109/IGARSS.2009.5418265]
  13. 13.
    Sun X X, Zhang J X, Yan Q and Gao J X.2011. A summary on current techniques and prospects of remote sensing change detection. Remote Sensing Information, (1): 119–123
  14. 14.
    Wei X L and Shen W M. 2010.Gabor-MRF model based on color texture image segmentation. Geomatics and Information Science of Wuhan University,35(8): 955–958
  15. 15.
    Wu C, Du B and Zhang L P. 2013. Slow feature analysis for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2858–2874
  16. 16.
    Wu Y Q, Cao Z Q and Tao F X. 2015.Change detection of remote sensing images by multi-scale geometric analysis and KICA. Journal of Remote Sensing, 19(1): 126–133
  17. 17.
    Xin F F, Jiao L C, Wang G T and Wan H L. 2012.Change detection in multi-temporal remote sensing images based on the wavelet-domain hidden Markov chain model. Journal of Xidian University(Natural Science), 39(3): 43–49
  18. 18.
    Zhang B. 2013. Research on the Sar Image Classification and Change Detection Based on the Markov Random Field. Wuhan: Wuhan University
  19. 19.
    Zhang H, Ni W P, Yan W D, Bian H, Wu J Z, Li S and Jin X. 2016. Mid and low resolution SAR image change detection based on fractal and multi-scale analysis. Geomatics and Information Science of Wuhan University, 41(5): 642–648

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