Abstract
Ship detection and classification is critical for national maritime security and national defense. As massive optical remote sensing images of high resolution are available, ship detection and classification on optical remote sensing images is becoming a promising technique, and has attracted great attention on applications including maritime security and traffic control. Some image processing-based methods have been proposed to detect ships in optical remote sensing images, but most of them face difficulty in terms of accuracy, performance and complexity. Therefore, in this paper, we propose a novel ship detection and classification approach which utilizes deep convolutional neural network (CNN) as the ship classifier. Next, in order to overcome the divergence problem of deep CNN-based classifier, a residual network-based ship classifier is proposed. In order to deepen the network without excessive growth of network complexity, inception layers are used. In addition, batch normalization is used in each convolution layer to accelerate the convergence. The performance of our proposed ship detection and classification approach is evaluated on a set of ship images downloaded from Google Earth, each in 256 × 64 pixels at the resolution 0.5 m. Ninety-five percent classification accuracy is achieved. A CUDA-enabled residual network is implemented in model training which achieved 75× speedup on 1 Nvidia Titan X GPU.
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Acknowledgments
This project was partially supported by Grants from Natural Science Foundation of China #71671178/#91546201. It was also supported by Hainan Provincial Department of Science and Technology under Grant No. ZDKJ2016021, and by Guangdong Provincial Science and Technology Project 20162016B010127004.
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Liu, Y., Cui, H., Li, G. (2017). A Novel Method for Ship Detection and Classification on Remote Sensing Images. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_63
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DOI: https://doi.org/10.1007/978-3-319-68612-7_63
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