A Deep Learning Approach for Ship Detection Using Satellite Imagery

Niranjan A. Patial S. Aryan A. Mittal A. Choudhury T. Rabiei-Dastjerdi H. Kumar P.
2024 European Alliance for Innovation

EAI Endorsed Transactions on Internet of Things
2024 #10

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 pre-processing, and utilizing a U-Net model trained on Sentinel-2 images. Data manipulation includes run-length encoding, masking, and balanced dataset pre-processing. RESULT: Results demonstrate the proposed deep learning models 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.

Copernicus Open Hub , Maritime Security , Run Length Encoding , Satellite imagery

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School of Computer Science, University of Petroleum & Energy Studies (UPES), Uttarakhand, Dehradun, 248007, India
Graphic Era Deemed to be University, Uttarakhand, Dehradun, 248002, India
School of Architecture Planning and Environmental Policy & CeADAR, University College Dublin (UCD) University College Dublin (UCD), Dublin, D04 V1W8, Ireland
Astana IT University, Turkistan Street, Astana, 020000, Kazakhstan

School of Computer Science
Graphic Era Deemed to be University
School of Architecture Planning and Environmental Policy & CeADAR
Astana IT University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026