Advertisement

Ship Target Detection in High-Resolution SAR Images Based on Information Theory and Harris Corner Detection

  • Haijiang WangEmail author
  • Yuanbo Ran
  • Shuo Liu
  • Yangyang Deng
  • Debin Su
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

In order to make up the shortcomings of the traditional CFAR detection algorithm, a ship target detection algorithm based on information theory and Harris corner detection for SAR images is proposed in this paper. Firstly, the SAR image is pretreated, and next, it is divided into superpixel patches by using the improved SLIC superpixel generation algorithm. Then, the self-information value of the superpixel patches is calculated and the threshold T1 is set to select the candidate superpixel patches. And then, the extended neighborhood weighted information entropy growth rate threshold T2 is set to eliminate false alarm detection results of the candidate superpixel patches. Finally, the Harris corner detection algorithm is used to process the detection result, the number of the corner threshold T3 is set to filter out the false alarm patches, and the final SAR image target detection result is obtained. The effectiveness and superiority of the proposed algorithm are verified by comparing the proposed method with the results of CFAR detection algorithm combining with morphological processing algorithm and information theory combining with morphological processing algorithm on the experimental high-resolution ship SAR images.

Keywords

SAR image Ship detection CFAR Superpixel Information theory and Harris corner 

References

  1. 1.
    Yeremy M. Ocean surveillance with polarimetric SAR. Can J Remote Sens. 2001;27(4):328–44.CrossRefGoogle Scholar
  2. 2.
    An W, Xie C, Yuan X. An improved iterative censoring scheme for CFAR ship detection with SAR imagery. IEEE Trans Geosci Remote Sens. 2014;52(8):4585–95.CrossRefGoogle Scholar
  3. 3.
    Gandhi PP, Kassam SA. Analysis of CFAR processors in homogeneous background. IEEE Trans Aerosp Electron Syst. 2002;24(4):427–45.CrossRefGoogle Scholar
  4. 4.
    Ren X, Malik J. Learning a classification model for segmentation. In: Proceedings of the IEEE international conference on computer vision, IEEE, vol.1; 2003. p. 10–7.Google Scholar
  5. 5.
    Yu W, Wang Y, Liu H, et al. Superpixel-based CFAR target detection for high-resolution SAR images. IEEE Geosci Remote Sens Lett. 2016;13(5):730–4.CrossRefGoogle Scholar
  6. 6.
    Wang X, Chen C. Ship detection for complex background SAR images based on a multiscale variance weighted image entropy method. IEEE Geosci Remote Sens Lett. 2017;14(2):184–7.CrossRefGoogle Scholar
  7. 7.
    Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell. 2012;34(11):2274.CrossRefGoogle Scholar
  8. 8.
    Cao Z, Ge Y, Feng J. Fast target detection method for high-resolution SAR images based on variance weighted information entropy. EURASIP J Adv Signal Process. 2014;2014(1):45.CrossRefGoogle Scholar
  9. 9.
    Harris C. A combined corner and edge detector. Proc Alvey Vision Conf. 1988;1988(3):147–51.Google Scholar
  10. 10.
    Wang Q. Inshore ship detection using high-resolution synthetic aperture radar images based on maximally stable extremal region. J Appl Remote Sens. 2015;9(1):095094.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Haijiang Wang
    • 1
    Email author
  • Yuanbo Ran
    • 1
  • Shuo Liu
    • 1
  • Yangyang Deng
    • 1
  • Debin Su
    • 1
  1. 1.College of Electronic Engineering, Chengdu University of Information TechnologyChengduChina

Personalised recommendations