SAR and AIS Data Fusion for Dense Shipping Environments
| Autor: | Raffaella Guida, Maximilian Rodger |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
010504 meteorology & atmospheric sciences Computer science fungi technology industry and agriculture 0211 other engineering and technologies 02 engineering and technology computer.software_genre Sensor fusion 01 natural sciences body regions Radar imaging Data mining False alarm skin and connective tissue diseases computer 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | IGARSS |
| DOI: | 10.1109/igarss39084.2020.9323254 |
| Popis: | A novel SAR-AIS data association technique is proposed consistent with being used in dense shipping environments, where association of SAR and AIS datasets is non-trivial and SAR false alarm rates are typically high. A ship classification model based on transfer learning classifies ship types in SAR imagery. The classification results are subsequently used in the SAR-AIS data association, which uses a rank-ordered assignment technique. The methodology is validated using a Sentinel-1 SAR product and terrestrial-based AIS product acquired from the Gulf Coast, USA. Results show optimal data association which is improved using class (i.e. ship type) information. |
| Databáze: | OpenAIRE |
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