Enhancement of vessel detection performance in SAR images generated academic advancements related to amelioration of the algorithmic accuracy and training data procurement. For practical implementation of vessel detection algorithm to maritime surveillance however, presentation of authentic position of vessels was essential. Accordingly, this study aimed to propose an algorithm which demonstrated realistic and azimuth shift-corrected position of vessel, especially out of conventional vessel monitoring apparatus: AIS and VPASS information. Two different analyses regarding the vessel detection output utilization were therefore presented. Primary analysis demonstrated a vessel identification algorithm, comparing the vessel detection output with elaborately preprocessed AIS and VPASS information, which indicated the discrete position and velocity of vessel. The other presented a position restoration algorithm via (i) velocity estimator complementing the conventional FrFT velocity estimation analysis, while investigating the effect of range acceleration in deriving the azimuth velocity and (ii) measuring the vessel orientation angle from Radon Transform. Both algorithms were implemented to the vessel detection output in Cosmo-SkyMed SAR images, depicting an enhanced accuracy compared to the conventional algorithm both in velocity estimation and azimuth shift estimation; velocity offset reduced from 1.64 m/s to 1.29 m/s and average azimuth shift offset reduced from 70.75 m to 62.39 m. The presented algorithms would be decisive in terms of practicality if robustly attached to CNN-based vessel detection algorithms demonstrating ideal detection performances.

Mitigation of geometric calibration offset in ocean without utilizing ground control points was investigated in this study. Real-time AIS information on vessels was exploited after preprocessing and accordingly tested against the detected vessels in the SAR image. Repetitive procedure of measuring the offset between the AIS sensor and the vessel detection was conducted and derived the SAR image of which the positioning offset was ameliorated. The proposed geo-location enhancement algorithm demonstrated the possibility of application in real-time vessel monitoring from remote sensing.

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