| Autor: |
Peder Heiselberg, Hasse B. Pedersen, Kristian A. Sorensen, Henning Heiselberg |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 6045-6054 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2024.3368508 |
| Popis: |
Satellite imagery has become a fundamental part for maritime monitoring and safety. Correctly estimating a ship's identity is a vital tool. We present a method based on facial recognition for identifying ships in satellite images. A large ship dataset is constructed from Sentinel-2 multispectral images and annotated by matching to the automatic identification system. Our dataset contains 7000 unique ships, for which a total of 16 000 images are acquired.The method uses a convolutional neural network to extract a feature vector from the ship images and embed it on a hypersphere. Distances between ships can then be calculated via the embedding vectors. The network is trained using a triplet loss function, such that minimum distances are achieved for identical ships and maximum distances to different ships. Comparing a ship image to a reference set of ship images yields a set of distances. Ranking the distances provides a list of the most similar ships. The method correctly identifies a ship on average 60% of the time as the first in the list. Larger ships are easier to identify than small ships, where the image resolution is a limitation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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