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SAR image change detection method of DSSRM based on cascade segmentation

  • role： First author第一作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

  • Email：jlzhang@mail.xidian.edu.cn
  • Introduction：张建龙(1976—　)，男，副教授，研究方向为遥感图像处理、模式识别理论和应用。E-mail：jlzhang@mail.xidian.edu.cn
ZHANG Jianlong,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

WANG Bin

Resümee

Synthetic Aperture Radar (SAR) images have region homogeneity with gray and texture. Considering that SRM (Statistical Region Merging) algorithms of image segmentation are efficient, stable and robust against noise, we propose a novel change detection method based on cascade segmentation with Dynamic Sorting Statistical Region Merging (DSSRM) algorithm. Firstly a DSSRM algorithm based on dynamic sorting is proposed to overcome conventional SRM's over-segmentation problem caused by single feature and static sorting. This algorithm takes the Manhattan distance of multi-feature of regions to be merged as the sorting criterion, and updates the adjacency matrix after each merging. Secondly based on the rule of minimizing mutual information we design a multi-channel complementary appearance model to improve the capability of constraint for region merging. Finally we present a cascade change detection framework with multiple levels. The first level projects difference image to super-pixel space via SRM, the second level utilizes DSSRM to dynamically merge regions; and the third level leverages a simplified SRM to realize region merging again to obtain final change detection map. Experimental results of the proposed method and proposed methods based on PCA and MRF are presented. By analysis and quantitative comparisons, the false alarm number and total of error number by DSSRMare decreased thereby the performance of KAPPA can get higher than methods based on PCA and MRF. DSSRM method is based on dynamic sorting algorithm with Manhattan distance of multi-feature of regions, it makes similar regions to be merged firstly. Experiments on construction of multi-channel illustrates that the more is the difference between channels the better is the performance of change detection. Our method improved the performance of SRM algorithm to avoid the over-segmentation phenomenon. Comparison experiments show that this method can obtain better performance of change detection than conventional SRM and state of art algorithms.

Schlüsselwort

SAR image;change detection;MDSSRM;cascade segmentation

References

  1. 1.
    Bovolo F and Bruzzone L. 2007. A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Transactions on GeoscienceandRemote Sensing, 45(1): 218–236
  2. 2.
    Celik T. 2009. Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4): 772–776
  3. 3.
    Coppin P, Jonckheere I, Nackaerts K, Muys B and Lambin E. 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25(9): 1565–1596
  4. 4.
    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
  5. 5.
    Feng J, Cao Z and Pi Y. 2013. Multiphase SAR image segmentation withG0-statistical-model-based activecontours. IEEE Transactions on Geoscience and Remote Sensing, 51(7): 4190–4199
  6. 6.
    Gao X B, Wang B, Tao D C and Li X L. 2011. A relay level set method for automatic image segmentation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(2): 518–525
  7. 7.
    Gong M G, Su L Z, Jia M and Chen W S. 2014. Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Transactions on Fuzzy Systems, 22(1): 98–109
  8. 8.
    Gou S P, Zhuang X, Zhu H M and Yu T T. 2013. Parallel sparse spectral clustering for SAR image segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4): 1949–1963
  9. 9.
    Hichri H, Bazi Y, Alajlan N and Malek S. 2013. Interactive segmentation for change detection in multispectral remote-sensing images. IEEE Geoscience and Remote Sensing Letters, 10(2): 298–302
  10. 10.
    Jia J H and Jiao L C. 2010. Image segmentation by spectral clustering algorithm with spatial coherence constraints. Journal of Infrared and Millimeter Waves, 29(1): 69–74
  11. 11.
    Lang F K, Yang J, Li D R, Zhao L L and Shi L. 2014. Polarimetric SAR image segmentation using statistical region merging. IEEE Geoscience and Remote Sensing Letters, 11(2): 509–513
  12. 12.
    Lu D, Mausel P, Brondízio E and Moran E. 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365–2407
  13. 13.
    Nock R and Nielsen F. 2004. Statistical region merging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11): 1452–1458
  14. 14.
    Salmon J. 2010. On two parameters for denoising with non-local means. IEEE Signal Processing Letters, 17(3): 269–272
  15. 15.
    Song X F, Wang S and Liu F. 2010. SAR image segmentation using Markov random field based on regions and Bayes belief Propagation. Acta Electronica Sinica, 38(12): 2810–2815
  16. 16.
    Soni V, Bhandari A K, Kumar A and Singh G K. 2013. Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms. IET Signal Processing, 7(8): 720–730
  17. 17.
    Wan H L, Jiao L C and Xin F F. 2012. Interactive segmentation technique and decision-level fusion based change detection for SAR image. Acta Geodaeticaet Cartographica Sinica, 41(1): 74–80
  18. 18.
    Wang B, Gao X B, Tao D C and Li X L. 2014. A nonlinear adaptive level set for image segmentation. IEEE Transactions on Cybernetics, 44(3): 418–428
  19. 19.
    Wang Y, Du L and Dai H. 2016. Unsupervised SAR image change detection based on SIFT keypoints and region information. IEEE Geoscience and Remote Sensing Letters, 13(7): 931–935
  20. 20.
    Xiong B L, Chen Q, Jiang Y M and Kuang G Y. 2012. A threshold selection method using two SAR change detection measures based on the Markov random field model. IEEE Geoscience and Remote Sensing Letters, 9(2): 287–291

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