Soft Computing

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

SAR image edge detection via sparse representation

Methodologies and Application

Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

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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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