Volume 52 Issue 6
Jun.  2026
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JIANG J,YAN W J,LIU K,et al. Ship target recognition method based on multi-source image fusion for unmanned aerial vehicle aerial photography[J]. Journal of Beijing University of Aeronautics and Astronautics,2026,52(6):1955-1964 (in Chinese)
Citation: JIANG J,YAN W J,LIU K,et al. Ship target recognition method based on multi-source image fusion for unmanned aerial vehicle aerial photography[J]. Journal of Beijing University of Aeronautics and Astronautics,2026,52(6):1955-1964 (in Chinese)

Ship target recognition method based on multi-source image fusion for unmanned aerial vehicle aerial photography

doi: 10.13700/j.bh.1001-5965.2024.0289
Funds:

National Natural Science Foundation of China (62371465); Shandong Province Youth Innovation Team(2022kj084); Shandong Provincial Natural Science Foundation(ZR2020QF010)

More Information
  • Corresponding author: E-mail: iamzlm@hotmail.com
  • Received Date: 07 May 2024
  • Accepted Date: 21 Jun 2024
  • Available Online: 24 Jun 2026
  • Publish Date: 15 Aug 2024
  • A multi-source ship image fusion recognition method is proposed for unmanned aerial vehicle aerial photography of multi-source ship images. In the face of many interferences in real scenes, pixel level fusion is adopted to fuse infrared and visible light ship images, and then perform target recognition. It can improve the algorithm's interpretability and lessen the network's reliance on samples as compared to feature level recognition techniques. This article focuses on solving the pixel offset caused by different sensor parameters. To eliminate the distortion and artifacts that traditional picture registration can readily cause, image registration has been turned into end-to-end feature alignment. A multi-source ship image fusion recognition network is proposed, which consists of a cross modulation feature extraction module, a feature dynamic alignment module, a multi granularity feature refinement module, and a pyramid feature fusion module. It can fully integrate the features and texture details of different modal images, effectively improving the recognition performance of ship targets. The approach described in this study has demonstrated great interpretability for multi-source images, good fusion performance, and high accuracy and robustness in recognizing ship targets through experimental verification.

     

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