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An effective deep learning model for ship detection from satellite images

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Abstract

Detecting ships from satellite images is a challenging task in the domain of remote sensing. It is very important for security, traffic management and to avoid smuggling etc. SAR (Synthetic Aperture Radar) is mostly used technology for Maritime monitoring but now researchers are increasingly studying Optical Satellite Images based technologies. Image processing and Computer Vision techniques were previously used to detect ships. In this work, Convolutional Neural Network based approach is used to detect ships from the satellite imagery. Several Deep Learning models have been used and tested for this kind of task. We used state of art model Inception-Resnet that is pre trained on Image-Net dataset. We used the dataset "Ships in Satellite Imagery" to detect the presence of ships in an image. The dataset is publicly available on Kaggle. The results indicate adoption of transfer learning and data augmentation yields a successful detection of ships with an accuracy of more than 99%. Similarly, exploring different deep learning models for this task provide results with high accuracy for less training time.

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References

  1. Zou, Z., & Shi, Z. (2016). Ship detection in spaceborne optical image with svd networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5832–5845.

    Article  Google Scholar 

  2. Liu, W., Ma, L., & Chen, H. (2018). Arbitrary-oriented ship detection framework in optical remote-sensing images. IEEE Geoscience and Remote Sensing Letters, 15(6), 937–941.

    Article  Google Scholar 

  3. Li, Q., Mou, L., Liu, Q., Wang, Y., & Zhu, X. X. (2018). Hsf-net: Multiscale deep feature embedding for ship detection in optical remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 99, 1–15.

    Article  Google Scholar 

  4. Wu, F., Zhou, Z., Wang, B., & Ma, J. (2018). Inshore ship detection based on convolutional neural network in optical satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

  5. Liu, Y., Zhang, M.-h., Xu, P., & Guo, Z.-w. (2017). Sar ship detection using sea-land segmentation-based convolutional neural network. In 2017 international workshop on remote sensing with intelligent processing (RSIP), pp. 1–4, IEEE

  6. Li, J., Qu, C., & J.Shao, C. (2017). Ship detection in sar images based on an improved faster r-cnn. In 2017 SAR in big data era: models, methods and applications (BIGSARDATA), pp. 1–6, IEEE.

  7. Jiao, J., Zhang, Y., Sun, H., Yang, X., Gao, X., Hong, W., Fu, K., & Sun, X. (2018). A densely connected end-to-end neural network for multiscale and multiscene sar ship detection. IEEE Access, 6, 20881–20892.

    Article  Google Scholar 

  8. Bentes, C., Frost, A., Velotto, D., & Tings, B. (2016). Ship-iceberg discrimination with convolutional neural networks in high resolution sar images. In Proceedings of EUSAR 2016: 11th European conference on synthetic aperture radar, pp. 1–4, VDE, 2016.

  9. Zhang, R., Yao, J., Zhang, K., Feng, C., & Zhang, J. (2016). S-cnn-based ship detection from high-resolution remote sensing images. In International archives of the photogrammetry, remote sensing & spatial information sciences, Vol. 41.

  10. Bentes, C., Velotto, D., & Tings, B. (2017). Ship classification in terrasar-x images with convolutional neural networks. IEEE Journal of Oceanic Engineering, 43(1), 258–266.

    Article  Google Scholar 

  11. An, Q., Pan, Z., & You, H. (2018). Ship detection in gaofen-3 sar images based on sea clutter distribution analysis and deep convolutional neural network. Sensors, 18(2), 334.

    Article  Google Scholar 

  12. Khan, H. M., & Yunze, C. (2018). Ship detection in SAR image using yolov2. In 2018 37th Chinese control conference (CCC), pp. 9495–9499, IEEE.

  13. Chang, Y.-L., Anagaw, A., Chang, L., Wang, Y. C., Hsiao, C.-Y., & Lee, W.-H. (2019). Ship detection based on yolov2 for sar imagery. Remote Sensing, 11(7), 786.

    Article  Google Scholar 

  14. Gallego, A.-J., Pertusa, A., & Gil, P. (2018). Automatic ship classification from optical aerial images with convolutional neural networks”. Remote Sensing, 10(4), 511.

    Article  Google Scholar 

  15. Liu, Z., Yuan, L., Weng, L., & Yang, Y. (2017). A high resolution optical satellite image dataset for ship recognition and some new baselines, pp. 324–331, 01 2017.

  16. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature 521.

  17. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85–117. 

    Article  Google Scholar 

  18. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). “Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.

    Google Scholar 

  19. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

    Article  Google Scholar 

  20. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).

  21. Simonyan, K., Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.

  22. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826.

  23. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708.

  24. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2012). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.

    Article  Google Scholar 

  25. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580–587, 2014

  26. Long, J., Shelhamer, E., Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440.

  27. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014). Largescale video classification with convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1725–1732.

  28. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.

  29. Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.

  30. Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 315–323.

  31. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. In European conference on computer vision, pp. 630–645, Springer, 2016.

  32. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.

  33. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255). IEEE, 2009.

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  1. Department of Computer Science, Bahria University, Shangrilla Road, Sector E-8, Islamabad, Pakistan

    Aaqib Mehran, Samabia Tehsin & Muhammad Hamza

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  1. Aaqib Mehran
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  2. Samabia Tehsin
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  3. Muhammad Hamza
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Correspondence to Samabia Tehsin.

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Mehran, A., Tehsin, S. & Hamza, M. An effective deep learning model for ship detection from satellite images. Spat. Inf. Res. 31, 61–72 (2023). https://doi.org/10.1007/s41324-022-00482-1

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