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Arbitrary-oriented ship detection based on Kullback-Leibler divergence regression in remote sensing images

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Abstract

Ship detection is a meaningful and challenging task in processing of remote sensing image data. Specifically, the main challenges faced by ship detection in remote sensing images (RSIs) include the variable direction and dense arrangement of ships, as well as the complex nearshore scenes. To address the above challenges, this article proposes an arbitrary-oriented ship detection network based on Kullback-Leibler divergence (KLD) regression. Firstly, a coordinate attention module (CAM) is designed to capture direction-aware and position-sensitive features, which enhances the attention to ships in complex scenes. Meanwhile, a reinforced feature fusion network (RFF-Net) combined with CAM is constructed to obtain fusion features containing rich semantic and location information. Then, the orientation-invariant model (OIM) is introduced to generate depth rotation-invariant features, which improves the adaptability of the network to arbitrary-oriented ships. Finally, a regression loss function based on KLD is defined to solve the problem of boundary discontinuity and realize the accurate detection of densely arranged ships. The experimental results demonstrate that the proposed method reaches 89.87% and 83.62% average precision (AP) on the HRSC2016 and DOTA Ship data sets, respectively, achieving state-of-the-art ship detection performance.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Funding

This work was supported by the National Natural Science Foundation of China [Grant No.: 61901081]; China Postdoctoral Science Foundation [Grant No.: 2020M680927]; Fundamental Research Funds for the Central Universities [Grant No.: 3132022237].

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Authors and Affiliations

  1. Department of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Yantong Chen, Jialiang Wang, Yanyan Zhang & Yang Liu

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  1. Yantong Chen
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  2. Jialiang Wang
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  3. Yanyan Zhang
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  4. Yang Liu
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Contributions

Yantong Chen and Jialiang Wang wrote the main manuscript text and designed the ship detection model. Yanyan Zhang and Yang Liu prepared Figs. 1-7 and performed the data processing and analysis. All authors reviewed the manuscript.

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Correspondence to Yantong Chen.

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Communicated by: H. Babaie.

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Chen, Y., Wang, J., Zhang, Y. et al. Arbitrary-oriented ship detection based on Kullback-Leibler divergence regression in remote sensing images. Earth Sci Inform 16, 3243–3255 (2023). https://doi.org/10.1007/s12145-023-01088-3

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