Faster R–CNN, RetinaNet and Single Shot Detector in different ResNet backbones for marine vessel detection using cross polarization C-band SAR imagery

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Detection of marine vessels plays an important role in monitoring, managing and securing seas and oceans, and forms the foundation of Maritime Domain Awareness (MDA). Although marine vessel detection has remained an active area of research for many years, unlike other object detectors, techniques of detection have been left far behind and lack systematic robustness. Hence, this study compared the performance of Faster R–CNN, RetinaNet and Single Shot Detector (SSD) across different epochs and complexities of ResNet architectures using Sentinel-1 VH polarization in one of the busiest ports in the Philippines. In particular, the models were created from the training samples dataset derived from Sentinel-1 VH imagery captured on January 12, 2024 in ResNet-34, -50, and −101 backbones, and 20 and 100 epochs. In this study, a total of 18 different object detector models were created for the comparative analysis. The models were tested with respect to different dates but having the same imagery type to determine their applicability across other base maps. Faster R–CNN with the highest F1 score of 0.85 outperformed RetinaNet with a highest F1 score of 0.74 and SSD with the highest F1 score of 0.38. The fastest model created was SSD, with an average speed of 9 to 44 minutes, followed by RetinaNet with an average speed of 8 to 58 minutes; the slowest is Faster R–CNN with an average speed of 25 minutes to 1 hour and 3 minutes. The use of Sentinel-1 VH imagery for marine vessel detection is a viable alternative, but the choice of object detectors should be carefully considered. The presence of geospatial software with advance deep learning tools improves remote sensing applications and allows non-programmers to optimize their competence. This study highlights the potential utilization of other imagery with higher spatial resolution, testing of other deep learning algorithms, finetuning of parameters, and utilization of higher computing infrastructure. The findings of this study can be applied in other areas for MDA, particularly in regions where advanced remote sensing applications have yet to be extensively explored.