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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (8): 29025-029025.doi: 10.7527/S1000-6893.2023.29025

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Progress of ship detection and recognition methods in optical remote sensing images

Qichang ZHAO1,2, Yiquan WU1(), Yubin YUAN1   

  1. 1.College of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2.Satellite General Department,Shanghai Satellite Engineering Research Institute,Shanghai 201109,China
  • Received:2023-05-22 Revised:2023-06-21 Accepted:2023-07-11 Online:2024-04-25 Published:2023-07-21
  • Contact: Yiquan WU E-mail:nuaaimage@163.com
  • Supported by:
    National Natural Science Foundation of China(61573183)

Abstract:

No matter in the field of military investigation or maritime law enforcement, the ship target detection and identification technology is very important. Especially with the development of optical remote sensing satellite, a large number of ship imaging data have been obtained, and how to quickly and accurately locate and identify ship targets from a large number of optical imaging data is a challenging work. Firstly, this paper summarizes the development process and technical process of ship target detection and recognition technology in optical remote sensing images. Then, the research progress in the acquisition and preprocessing of optical remote sensing images, the separation of land and sea, the detection of ship targets and the recognition of ship targets are reviewed, and the methods and progress of target detection and recognition of optical remote sensing images using traditional and deep learning methods are emphatically discussed. Then, 11 kinds of remote sensing image data sets including ship targets and performance evaluation indexes are introduced. Finally, the main problems in ship target detection and recognition technology are analyzed, and the future development direction of ship target detection and recognition technology is given.

Key words: optical remote sensing, remote sensing image, sea-land segmentation, ship detection, ship recognition

CLC Number: