Abstract
The automatic detection of the ship from satellite image analysis is the limelight of research in recent years due to its widespread applications. In this paper, a handful of traditional machine learning and deep learning models are compared based on their performance to classify the satellite images available in the public repository as a ship or other categories. The Support Vector Machine(SVM), Decision Trees, Random Forest, K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GaussianNB), and Logistic Regression are machine learning models used in the present work. Histogram of Gradient (HoG) features are used as feature descriptors considering the diverse size and shape of ships in the satellite image dataset. Transfer learning is applied using the deep learning models namely, Inception and ResNet, that are fine-tuned for various learning rates and optimizers. The meticulous experimentation carried out reveals that traditional machine learning performs well when trained and tested on a single dataset. However, there is a drastic change in the performance of machine learning models when tested on a different ship dataset. The results show that the deep learning models have better feature detection and thus have better performance when transfer learning is used on various datasets.
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References
Lázaro, F., Raulefs, R., Wang, W., Clazzer, F., Plass, S.: VHF data exchange system (VDES): an enabling technology for maritime communications. CEAS Space J. 11(1), 55–63 (2019). https://doi.org/10.1007/s12567-018-0214-8
Tetreault, B.: Use of the automatic identification system (AIS) for maritime domain awareness (MDA). In: Proceedings of OCEANS 2005 MTS/IEEE, vol. 2, pp. 1590–1594 (2005)
Ford, J.H., Peel, D., Kroodsma, D., Hardesty, B.D., Rosebrock, U., Wilcox, C.: Detecting suspicious activities at sea based on anomalies in automatic identification systems transmissions. PLOS ONE 13(8), e0201640 (2018). https://doi.org/10.1371/journal.pone.0201640
Kamirul, K., Hasbi, W., Hakim, P., Syafrudin, A.H.: Automatic ship recognition chain on satellite multispectral imagery. IEEE Access 8, 221918–221931 (2020). https://doi.org/10.1109/ACCESS.2020.3042702
Probst, P., Boulesteix, A.L., Bischl, B.: Tunability: importance of hyperparameters of machine learning algorithms. J. Mach. Learn. Res. 20(53), 1–32 (2019)
Sarker, I.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2, 160 (2021)
Yang, G., Li, B., Ji, S., Gao, F., Xu, Q.: Ship detection from optical satellite images based on sea surface analysis. IEEE Geosci. Remote Sens. Lett. 11(3), 641–645 (2014). https://doi.org/10.1109/LGRS.2013.2273552
Chua, M., Aha, D.W., Auslander, B., Gupta, K., Morris, B.: Comparison of object detection algorithms on maritime vessels. Tech. rep, Naval Research Lab, Washington, D.C. (2014)
Mohamed, N.A., Zulkifley, M.A., Zaki, W.M.D.W., Hussain, A.: An automated glaucoma screening system using cup-to-disc ratio via simple linear iterative clustering superpixel approach. Biomed. Signal Process. Control 53, 101454 (2019). https://doi.org/10.1016/j.bspc.2019.01.003
Cordova, A.W.A., Quispe, W.C., Inca, R.J.C., Choquehuayta, W.N., Gutierrez, E.C.: New approaches and tools for ship detection in optical satellite imagery. J. Phys. Conf. Ser. 1642, 012003 (2020). https://doi.org/10.1088/1742-6596/1642/1/012003
Li, Z., Zhao, L., Han, X., Pan, M.: Lightweight ship detection methods based on yolov3 and densenet. Math. Prob. Eng. 2020, 4813183 (2020). https://doi.org/10.1155/2020/4813183
Liu, C., Zhu, W.: An improved algorithm for ship detection in SAR images based on CNN. In: Pan, Z., Hei, X. (eds.) Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), vol. 11720, pp. 63–71. International Society for Optics and Photonics, SPIE (2021). https://doi.org/10.1117/12.2589421.
Stofa, M.M., Zulkifley, M.A., Zaki, S.Z.M.: A deep learning approach to ship detection using satellite imagery. IOP Conf. Ser. Earth Environ. Sci. 540, 012049 (2020). https://doi.org/10.1088/1755-1315/540/1/012049
Wang, Y., Ning, X., Leng, B., Fu, H.: Ship detection based on deep learning. In: 2019 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 275–279 (2019). https://doi.org/10.1109/ICMA.2019.8816265
Gallego, A.J., Pertusa, A., Gil, P.: Automatic ship classification from optical aerial images with convolutional neural networks. Remote Sens. 10(4) (2018). https://doi.org/10.3390/rs10040511
Liu, Y., Cui, H.Y., Kuang, Z., Li, G.: Ship detection and classification on optical remote sensing images using deep learning. ITM Web Conf. 12, 05012 (2017). https://doi.org/10.1051/itmconf/20171205012
Zou, Z., Shi, Z.: Ship detection in spaceborne optical image with SVD networks. IEEE Trans. Geosci. Remote Sens. 54(10), 5832–5845 (2016). https://doi.org/10.1109/TGRS.2016.2572736
Tang, J., Deng, C., Huang, G.B., Zhao, B.: Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans. Geosci. Remote Sens. 53(3), 1174–1185 (2015). https://doi.org/10.1109/TGRS.2014.2335751
Rhammell. Ships in satellite imagery-v1 (2018). https://www.kaggle.com/rhammell/ships-in-satellite-imagery/metadata. Accessed 06 Apr 2021
Airbus. Airbus ship detection challenge (2018). https://www.kaggle.com/c/airbus-ship-detection/overview. Accessed 25 Apr 2021
Eum, H., Bae, J., Yoon, C., Kim, E.: Ship detection using edge-based segmentation and histogram of oriented gradient with ship size ratio. IJFIS 15, 251–259 (2015). https://doi.org/10.5391/IJFIS.2015.15.4.251
Evgeniou, T., Pontil, M.: Support vector machines: theory and applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 249–257. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44673-7_12
Peng, C.Y.J., Lee, K.L., Ingersoll, G.M.: An introduction to logistic regression analysis and reporting. J. Educ. Res. 96(1), 3–14 (2002). https://doi.org/10.1080/00220670209598786
Kaviani, P., Dhotre, S.: Short survey on Naive Bayes algorithm. Int. J. Adv. Res. Comput. Sci. Manag. 4 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CVPR 2016, 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CVPR2016 (2016). https://doi.org/10.1109/CVPR.2016.308
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. arXiv preprint arXiv:1608.06993 (2016)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626 (2017). https://doi.org/10.1109/ICCV.2017.74
Acknowledgements
Dr. P. Jidesh wish to thank the Science and Engineering Research Board, India for providing financial support under the research grant No. CRG/2020/000476. The other authors (Mr. Abhinaba and Ms. Smitha) wish to thank Ministry of Education (MoE) for providing financial support under the scholarship scheme to carryout the research at National Institute of Technology Karnataka, Surathkal. Authors would like to acknowledge the creators of the datasets [19, 20] mentioned in this paper and making their dataset publicly available.
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Hazarika, A., Jidesh, P., Smitha, A. (2022). Comparative Analysis of Machine Learning and Deep Learning Models for Ship Classification from Satellite Images. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_6
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