@article{Yin_2023,
doi = {10.1088/1742-6596/2486/1/012018},
url = {https://dx.doi.org/10.1088/1742-6596/2486/1/012018},
year = {2023},
month = {may},
publisher = {IOP Publishing},
volume = {2486},
number = {1},
pages = {012018},
author = {Z Y Yin and Y Q Tang and Y Z Chen and Y Y Zhang},
title = {A Ship Monitoring Framework Based on Multimodal Remote Sensing Data},
journal = {Journal of Physics: Conference Series},
abstract = {Due to the wide monitoring range, remote sensing satellites have more advantages than ground monitoring in large-scale monitoring. In particular, satellite network observations make rapid and frequent ground monitoring possible. In this paper, an all-day and all-weather marine ship monitoring framework based on multimodal remote sensing data was established. Scene recognition method was first used to segment sea areas. Then, we analyzed the ship characteristics of different data and used them for ship detection. Finally, the motion state of the ship was judged and the dynamic ships in the video were tracked. To prove the proposed framework, the data of Sentinel-1/2 and Jilin-1 data were used for verification. The experimental results demonstrated the advantages of the proposed framework for ship monitoring, which achieved the purpose of ship detection and tracking.}
}