AIS Data-Guided Geolocation Correction Method for Low-Orbit Satellite Remote Sensing Imagery

Autor: Haoyang Wu, Zishuo Huang, Qinyou Hu, Xin Ran, Qiang Mei
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18703-18726 (2024)
Druh dokumentu: article
ISSN: 1939-1404
2151-1535
DOI: 10.1109/JSTARS.2024.3470903
Popis: Accurate geolocation of maritime objects in satellite imagery is challenging due to geometric distortions, atmospheric conditions, and sensor inaccuracies in low-Earth orbit satellites. This study presents a novel automatic identification system (AIS) data-guided geolocation correction method that integrates real-time AIS data with satellite imagery to rectify geolocation errors. The approach utilizes the GeoAISNet neural network, which enhances positional accuracy without relying on ground control points. By incorporating a modified YOLOv8 architecture with orientation parameters and the convolutional block attention module, detection performance improved significantly, achieving precision, recall, and F1 scores of 91.82%, 89.56%, and 90.67%, respectively. Ablation studies demonstrated the crucial impact of feature integration and attention mechanisms. Results indicate a mean average precision of 89%, with general cargo ships achieving 99.9% AP50. Localization accuracy saw a notable improvement, with root-mean-squared error reduced from 12 to 3 m, and layer normalization further enhanced stability, increasing precision, recall, and F1 scores to 94.23%, 92.67%, and 93.44%, respectively. The use of differential AIS data decreased maximum positional errors by 30%, achieving errors around 2 m. Computational efficiency was also enhanced, with processing time reduced from 2 to 0.5 s per image. This method effectively addresses oil spills and non-AIS vessel detection, expanding maritime surveillance capabilities. The global training dataset, validated with data from the South China Sea, ensures the method's applicability across diverse conditions.
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