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Ship Detection Algorithm for SAR Images Based on Lightweight Convolutional Network

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Abstract

Although ship detectors in synthetic aperture radar (SAR) images have continuously advanced the state-of-the-art performance in recent years. It is still difficult to balance the accuracy and efficiency. In this paper, we propose a ship detection algorithm for SAR images based on lightweight convolutional network. First, the Top-hat layer is designed by introducing the Top-hat operator, and Region Proposal Network (RPN) is constructed based on the layer to conduct rapid screening of SAR ship candidate regions. Second, the Facebook Berkeley Nets (FBNet) is introduced to accurately locate the SAR ship target in the candidate region and the Differential Neural Architecture Search technology is used to optimize the parameters of the network structure. Finally, the proposed ship detection framework is validated on the SAR ship datasets with other methods.

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Data Availability

This article uses public dataset AIR-SARShip-1.0, which can be downloaded directly on the Internet.

Code Availability

If readers are interested in the code involved in this article, they can contact the corresponding author.

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Funding

This work was supported by the national natural science foundation of China (Grant No. 91738302).

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, 100081, China

    Yun Wang, Hao Shi & Liang Chen

  2. Shanghai Academy of Spaceflight Technology, Shanghai, 201109, China

    Yun Wang

Authors
  1. Yun Wang
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  2. Hao Shi
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  3. Liang Chen
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Contributions

YW and HS conceived the main innovation; YW and LC conceived and designed the experiments; HS and LC performed the experiments; HS analyzed the data; and YW wrote the paper.

Corresponding author

Correspondence to Hao Shi.

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Wang, Y., Shi, H. & Chen, L. Ship Detection Algorithm for SAR Images Based on Lightweight Convolutional Network. J Indian Soc Remote Sens 50, 867–876 (2022). https://doi.org/10.1007/s12524-022-01491-1

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