Soft Computing

, Volume 22, Issue 8, pp 2507–2515 | Cite as

SAR image edge detection via sparse representation

  • Xiaole Ma
  • Shuaiqi LiuEmail author
  • Shaohai HuEmail author
  • Peng Geng
  • Ming Liu
  • Jie Zhao
Methodologies and Application


In this paper, we propose a new synthetic aperture radar (SAR) image detection algorithm based on the de-noising algorithm via the sparse representation and a new morphology edge detector. Firstly, we apply the Shearlet transform to the SAR image to get the sparse representation of it. Then, morphological edge detector with direction is applied to directional sub-band coefficients of the Shearlet which are recovered by the iterative de-noising process. Finally, the completed SAR image edge is obtained by merging each sub-band edge using Dempster–Shafer evidence theory. By completely using the directional sub-bands of the Shearlet transform, the proposed algorithm overcomes the disadvantages of transform detection algorithms which are very unrobust to noise and can also generate inaccurate edges. The experimental results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the edge positioning accuracy, integrity, and the number of false edge points.


SAR image edge detection Sparse representation Shearlet Morphology edge detector DS theory 



This work was supported in part by Natural Science Foundation of China under Grant 61401308 and 61572063, Natural Science Foundation of Hebei Province under Grant F2016201142 and F2016201187, Natural Social Foundation of Hebei Province under Grant HB15TQ015, Science research project of Hebei Province under Grant QN2016085 and ZC2016040, Science and technology support project of Hebei Province under Grant 15210409, Natural Science Foundation of Hebei University under Grant 2014-303, National Comprehensive Ability Promotion Project of Western and Central China.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Ethical approval

This paper does not contain any studies with human participants performed by any of the authors.


  1. Chen BJ, Shu HZ, Coatrieux G, Chen G, Sun XM, Coatrieux JL (2015) Color image analysis by quaternion-type moments. J Math Imaging Vis 51(1):124–144MathSciNetCrossRefzbMATHGoogle Scholar
  2. Dai M, Peng C, Chan AK (2004) Bayesian wavelet shrinkage with edge detection for sar image despeckling. IEEE Trans Geosci Remote Sens 42(8):1642–1648CrossRefGoogle Scholar
  3. Guo K, Labate D (2007) Optimally sparse multidimensional representation using shearlets. SIAM J Math Anal 39(1):298–318MathSciNetCrossRefzbMATHGoogle Scholar
  4. Li QW, Huo GY, Li H (2012a) Bionic vision-based synthetic aperture radar image edge detection method in non-subsampled contourlet transform domain. IET Radar Sonar Navig 6(6):526–535CrossRefGoogle Scholar
  5. Li QW, Huo GY, Li H (2012b) Special section on biologically-inspired radar and sonar systems-bionic vision-based synthetic aperture radar image edge detection method in non-subsampled contourlet transform domain. IET Radar Sonar Navig 6(6):526–535CrossRefGoogle Scholar
  6. Li J, Li XL, Yang B, Sun XM (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  7. Lim WQ (2010) The discrete shearlets transform: a new directional transform and compactly supported shearlets frames. IEEE Trans Image Process 19(5):1166–1180MathSciNetCrossRefzbMATHGoogle Scholar
  8. Liu SQ, Hu SH, Xiao Y (2012) Sar image edge detection based on local hybrid filter. J Electron Inf Technol 35(5):1120–1127CrossRefGoogle Scholar
  9. Liu SQ, Hu SH, Xiao Y (2014) Bayesian shearlet shrinkage for sar image de-noising via sparse representation. Multidimens Syst Signal Process 25(4):683–701CrossRefGoogle Scholar
  10. Liu HP, Liu YH, Sun FC (2015) Robust exemplar extraction using structured sparse coding. IEEE Trans Neural Netw Learn Syst 26(8):1816–1821MathSciNetCrossRefGoogle Scholar
  11. Liu HP, Guo D, Sun FC (2016) Object recognition using tactile measurements: Kernel sparse coding methods. IEEE Trans Instrum Meas 65(3):656–665CrossRefGoogle Scholar
  12. Pan ZP, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcasting 61(2):166–176CrossRefGoogle Scholar
  13. Pauwels R, Jacobs R, Bosmans H (2014) Automated implant segmentation in cone-beam ct using edge detection and particle counting. Int J Comput Assist Radiol Surg 9(4):733–743CrossRefGoogle Scholar
  14. Ranjani JJ, Gokila M, Thiruvengadam SJ (2008) Edge detection in speckled sar images with improved roewa. In: ICVGIP ’08, pp 644–649Google Scholar
  15. Sheng Y, Raleigh NC, Labate D (2009) A shearlet approach to edge analysis and detection. IEEE Trans Image Process 18(5):1057–7149MathSciNetCrossRefGoogle Scholar
  16. Umbaugh SE (2010) Digital image processing and analysis : human and computer vision applications with cviptools, 2nd edn. CRC PressGoogle Scholar
  17. Wang JZ (2011) Lane detection of multi-visual-features fusion based on d-s theory. In: Proceedings of the 30th Chinese Control Conference (CCC 2011), pp 3047–3052Google Scholar
  18. Xia ZH, Wang XH, Sun XM, Liu QS, Xiong NX (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl 75(4):1947–1962CrossRefGoogle Scholar
  19. Xu YL, Tian S, Li JW (2012) Sar image despeckling based on edge detection and plural pervasion equation in shearlet domain. J Xidian Univ 39(6):166–171Google Scholar
  20. Yang SB, Peng FY (2008) Multidirectional morphological edge detection algorithm based on alternate filtering. ISTIA 2008:1223–1226Google Scholar
  21. Zhang YJ, Han QR (2011) Edge detection algorithm based on wavelet transform and mathematical morphology. CASE 2011:1–3Google Scholar
  22. Zhao RZ, Liu XY, Li CC (2009) Wavelet denoising via sparse representation. Sci China Ser F 52(8):1371–1377MathSciNetCrossRefzbMATHGoogle Scholar
  23. Zheng YH, Jeon B, Xu DH, Wu QJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina
  3. 3.College of Electronic and Information EngineeringHebei UniversityBaodingChina
  4. 4.Key Laboratory of Digital Medical Engineering of Hebei ProvinceBaodingChina
  5. 5.College of Information Science and TechnologyShijiazhuang Tiedao UniversityShijiazhuangChina
  6. 6.Department of PersonnelHebei UniversityBaodingChina

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