Seung Jae Lee, Ji Won Yoon

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 8, pp. 311-318, Aug. 2021
https://doi.org/10.3745/KTSDE.2021.10.8.311,   PDF Download:
Keywords: Coastline Extraction, satellite image, R-CNN, watershed algorithm
Abstract

Research has been ongoing to detect ships from offshore photographs for a variety of reasons, including maritime security, identifying international trends, and social scientific research. Due to the development of artificial intelligence, R-CNN models for object detection in photographs and images have emerged, and the performance of object detection has risen dramatically. Ship detection in offshore photographs using the R-CNN model has also begun to apply to satellite photography. However, satellite images project large areas, so various objects such as vehicles, landforms, and buildings are sometimes recognized as ships. In this paper, we propose a novel methodology to improve the performance of ship detection in satellite photographs using R-CNN series models. We separate land and sea via marker-based watershed algorithm and perform morphology operations to specify RoI one more time, then detect vessels using R-CNN family models on specific RoI to reduce typology. Using this method, we could reduce the misdetection rate by 80% compared to using only the Fast R-CNN.

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