access icon openaccess Multiscale saliency detection method for ship targets in synthetic aperture radar images

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 21/02/2019, Accepted 07/05/2019, Published 11/07/2019

Ship detection is an important direction of synthetic aperture radar (SAR) image application in maritime surveillance. A multi-scale optimisation threshold saliency detection is developed in this letter, which is used to detect ships targets of SAR images. The SAR image is first decomposed into a pyramid image sequence. Then the saliency detection is performed using the spectral residual method for each layer in the sequence, and the salient sub-graph that contains ship targets is obtained. Finally, each sub-graph is fused and the optimisation threshold segmentation method that applies to the saliency map gives the final detection result. Experimental results show that the proposed approach has low complexity and high detection accuracy and greatly reduces the dependence on prior knowledge.

Inspec keywords: radar detection; ships; optimisation; image fusion; object detection; image segmentation; radar imaging; marine radar; graph theory; image sequences; synthetic aperture radar

Other keywords: multiscale optimisation threshold saliency detection; synthetic aperture radar image application; SAR image application; spectral residual method; multiscale saliency detection method; maritime surveillance; optimisation threshold segmentation method; pyramid image sequence; ship target detection

Subjects: Signal detection; Radar equipment, systems and applications; Combinatorial mathematics; Optimisation techniques; Optical, image and video signal processing

References

    1. 1)
      • 11. Pourmottaghi, A., Taban, M.R., Gazor, S.: ‘A CFAR detector in a nonhomogenous weibull clutter’, IEEE Trans. Aerosp. Electron. Syst., 2012, 48, (2), pp. 1747–1758.
    2. 2)
      • 9. Itti, L., Koch, C., Niebur, E.: ‘A model of saliency-based visual attention for rapid scene analysis’, IEEE Trans. Pattern Analysis Mach. Intell., 1998, 20, (11), pp. 1254–1259.
    3. 3)
      • 5. Gao, G., Liu, L., Zhao, L., et al: ‘An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images’, IEEE Trans. Geosci. Remote Sens., 2009, 47, (6), pp. 1685–1697.
    4. 4)
      • 12. Wang, X., Chen, C.: ‘Adaptive ship detection in SAR images using variance WIE-based method’, Signal Image Video Process., 2016, 10, (7), pp. 1–6.
    5. 5)
      • 6. Yonggang, J.I., Jie, Z., Meng, J., et al: ‘A new CFAR ship target detection method in SAR imagery’, Acta Oceanologica Sinica, 2010, 29, (1), pp. 12–16.
    6. 6)
      • 2. Eldhuset, K.: ‘An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions’, IEEE Trans. Geosci. Remote Sens., 1996, 34, (4), pp. 1010–1019.
    7. 7)
      • 8. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybern., 2007, 9, (1), pp. 62–66.
    8. 8)
      • 10. Hou, X., Zhang, L.: ‘Saliency detection: A spectral residual approach’, Comput. Vis. Pattern Recognit., 2007, pp. 1–8.
    9. 9)
      • 1. Brusch, S., Lehner, S., Fritz, T., et al: ‘Ship surveillance with TerraSAR-X’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (3), pp. 1092–1103.
    10. 10)
      • 7. Jin, M.K., Chen, K.S.: ‘The application of wavelets correlator for ship wake detection in SAR images’, IEEE Trans. Geosci. Remote Sens., 2003, 41, (6), pp. 1506–1511.
    11. 11)
      • 4. Zaimbashi, A., Norouzi, Y.: ‘Automatic dual censoring cell-averaging CFAR detector in non-homogenous environments’ (Elsevier North-Holland Inc., Amsterdam, Netherlands, 2008).
    12. 12)
      • 3. Jiang, Q., Wang, S., Ziou, D., et al: ‘Ship detection in RADARSAT SAR imagery’. IEEE Int. Conf. on Systems, Man, and Cybernetics, San Diego, CA, USA, 1998, 5, pp. 4562–4566.
    13. 13)
      • 13. Wang, X., Chen, C.: ‘Ship detection for Complex background SAR images based on a multiscale variance weighted image entropy method’, IEEE Trans. Geosci. Remote Sens., 2017, 14, (2), pp. 184–187.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2019.0556
Loading

Related content

content/journals/10.1049/joe.2019.0556
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading