Abstract
INTRODUCTION: This paper addresses ship detection in satellite imagery through a deep learning approach, vital for maritime applications. Traditional methods face challenges with large datasets, motivating the adoption of deep learning techniques.
 OBJECTIVES: The primary objective is to present an algorithmic methodology for U-Net model training, focusing on achieving accuracy, efficiency, and robust ship detection. Overcoming manual limitations and enhancing real-time monitoring capabilities are key objectives.
 METHOD: The methodology involves dataset collection from Copernicus Open Hub, employing run-length encoding for efficient preprocessing, and utilizing a U-Net model trained on Sentinel-2 images. Data manipulation includes run-length encoding, masking, and balanced dataset preprocessing.
 RESULT: Results demonstrate the proposed deep learning model's effectiveness in handling diverse datasets, ensuring accuracy through U-Net architecture, and addressing imbalances. The algorithmic process showcases proficiency in ship detection.
 CONCLUSION: In conclusion, this paper contributes a comprehensive methodology for ship detection, significantly advancing accuracy, efficiency, and robustness in maritime applications. The U-Net-based model successfully automates ship detection, promising real-time monitoring enhancements and improved maritime security.
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