A Deep Learning Approach for Ship Detection Using Satellite Imagery

• Published in: EAI endorsed transactions on internet of things Vol. 10
• Main Authors: Niranjan, Alakh, Patial, Sparsh, Aryan, Aditya, Mittal, Akshat, Choudhury, Tanupriya, Rabiei-Dastjerdi, Hamidreza, Kumar, Praveen
• Format: Journal Article
• Language: English
• Published: 15.03.2024
• ISSN: 2414-1399; 2414-1399
• DOI: 10.4108/eetiot.5435

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.