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

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Communications, Signal Processing, and Systems (CSPS 2018)

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

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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.

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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.

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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
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  2. Zongyong Cui
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  3. Zongjie Cao
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  4. Rui Min
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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

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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

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

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

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