ShipDC: a new baseline for optical remote sensing ship detection and classification

Li Lin; Zongji Zhao

doi:10.1117/12.3019841

23 January 2024 ShipDC: a new baseline for optical remote sensing ship detection and classification

Li Lin, Zongji Zhao

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129781M (2024) https://doi.org/10.1117/12.3019841
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Recently, ship detection in satellite remote sensing images has gradually become one of the most popular research in the field of remote sensing. In maritime traffic, maritime rescue, and military operations, ship detection based on satellite remote sensing images has important application value. Optical satellite remote sensing has the characteristics of high resolution, strong detection capability, and easy for human interpretation. It has important application significance in both military and civilian fields. In order to respond to the needs of practical applications, we constructed a ship object detection dataset with the Gaofen-2 satellite images. We optimize and improve the existing deep learning network according to the characteristics of optical satellite remote sensing images, and proposes a high-resolution optical satellite remote sensing image ship object detection and classification algorithm based on YOLOv5. In addition, a channel attention module is used to improve the network's ability to represent features. We conduct experiments on the proposed dataset, and the results show that the algorithm has the ability to detect large maritime ships and to classify multicategory military ships.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Lin and Zongji Zhao "ShipDC: a new baseline for optical remote sensing ship detection and classification", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129781M (23 January 2024); https://doi.org/10.1117/12.3019841

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