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Ship Detection in Navy Using Satellite Images by Deep Learning Approach
Ship Detection in Navy Using Satellite Images by Deep Learning Approach
Dwarakanath G V1, Akshay B R2
1Assistant Professor, Department of Master of Computer Application, BMS Institute of Technology and Management, Bengaluru, Karnataka
2Student, Department of Master of Computer Application, BMS Institute of Technology and Management, Bengaluru, Karnataka
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Abstract - Ship identification in satellite images poses significant challenges within the domain of remote sensing. Its importance extends to critical areas such as security, encompassing concerns such as military attacks, accidents, illegal transportation of goods, illegal fishing, territorial violations, and ship hijackings. Additionally, effective traffic management and smuggling prevention heavily rely on accurate ship identification. While synthetic aperture radar (SAR) has historically dominated maritime monitoring, researchers are increasingly exploring optical satellite images as a potential alternative. Previous ship detection techniques have utilized Computer-based image processing vision methods. However, this study proposes a novel approach employing a Convolutional Neural Network (CNN) based method to accurately identify ships in satellite data. The suggested approach entails the utilization and assessment of a custom-designed deep learning model based on CNN architecture. to recognize ships in satellite photos.
Keywords - ship detection, optical images, Convolutional Neural Networks.
