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

  • Xiulan TanEmail author
  • Zongyong Cui
  • Zongjie Cao
  • Rui Min
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


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.


SAR Ship detection Superpixel Random forest Clustering 



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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xiulan Tan
    • 1
    Email author
  • Zongyong Cui
    • 1
  • Zongjie Cao
    • 1
  • Rui Min
    • 1
  1. 1.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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