Skip to main content
Springer Nature Link
Log in

Ship Detection via Superpixel-Random Forest Method in High-Resolution SAR Images

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

  • 2374 Accesses

Abstract

With the increasing resolution of synthetic aperture radar (SAR), the traditional SAR image target detection methods used for medium-low resolution are not suitable for high-resolution SAR images, which contain detailed information about structure, shape, and weak echoes that are hardly detected in traditional ways. In this paper, we proposed a new method, Superpixel-Random Forest Technique, to detect ships in high-resolution SAR images. The method combines superpixel and random forest algorithms. The superpixel is adopted to divide images into many subregions properly, and the random forest is used for unsupervised clustering these subregions into ships or others. The experimental results show that the algorithm can accurately detect the ship targets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhou X, Chang NB, Li S. Applications of SAR interferometry in earth and environmental science research. Sensors. 2009;9(3):1876–912.

    Article  Google Scholar 

  2. Hou B, Chen X, Jiao L. Multilayer CFAR detection of ship targets in very high resolution SAR images. IEEE Geosci Remote Sens Lett. 2014;12(4):811–5.

    Google Scholar 

  3. Hansen VG. Constant false alarm rate processing in search radars. Radar-present and future 1973.

    Google Scholar 

  4. Wang S, et al. New hierarchical saliency filtering for fast ship detection in high-resolution SAR images. IEEE Trans Geosci Remote Sens. 2016;55(1):351–62.

    Article  Google Scholar 

  5. Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell. 2002;24(5):603–19.

    Article  Google Scholar 

  6. Nock R, Nielsen F. Statistical region merging. IEEE Trans Pattern Anal Mach Intell. 2004;26(11):1452.

    Article  Google Scholar 

  7. Achanta R, et al. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell. 2012;34(11):2274–82.

    Article  Google Scholar 

  8. Qin F, et al. Superpixel segmentation for polarimetric SAR imagery using local iterative clustering. IEEE Geosci Remote Sens Lett. 2017;12(1):13–7.

    Google Scholar 

  9. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by the Key Technology R&D Program of Sichuan Province 2015GZ0109, the National Nature Science Foundation of China under Grant 61271287 and Grant U14331.

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China

    Xiulan Tan, Zongyong Cui, Zongjie Cao & Rui Min

Authors
  1. Xiulan Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zongyong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zongjie Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Min
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiulan Tan .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tan, X., Cui, Z., Cao, Z., Min, R. (2020). Ship Detection via Superpixel-Random Forest Method in High-Resolution SAR Images. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_85

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6504-1_85

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics