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Ship detection based on deep learning using SAR imagery: a systematic literature review

  • Foundation, algebraic, and analytical methods in soft computing
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Abstract

This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research.

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Acknowledgements

We gratefully appreciate the editor and anonymous reviewers for their efforts and constructive comments, which have greatly improved the technical quality and presentation of this study.

Funding

National Key Research and Development Program of China (2017YFC1405600).

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Correspondence to Wan Jianhua.

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Yasir, M., Jianhua, W., Mingming, X. et al. Ship detection based on deep learning using SAR imagery: a systematic literature review. Soft Comput 27, 63–84 (2023). https://doi.org/10.1007/s00500-022-07522-w

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  • DOI: https://doi.org/10.1007/s00500-022-07522-w

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