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Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest
https://doi.org/10.22146/ijccs.37461 Endang Anggiratih(1), Agfianto Eko Putra(2*)
(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta, Indonesia
(*) Corresponding Author
Abstract
Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolutional Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%.
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[1] W. Guo, X. Xia, and W. Xiaofei, “A remote sensing ship recognition method based on dynamic probability generative model”, Expert System with Applications, vol. 41, issue 14, Oct 2014 [online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417414001705. [Accessed: 22-Mar-2017]
[2] L. Chunyan, Z. Huanxin, S. Hao, and Z. Shilin, “Combing Rough Set and RBF Neural Network for Large-Scale Ship Recognition in Optical Satellite Images”, IOP Conference Series: Earth and Environmental, vol 17, con 1, 2014 [Online]. Available: http://iopscience.iop.org/article/10.1088/1755-1315/17/1/012218/pdf. [Accessed: 22-Mar-2017]
[3] G. Liu, Y. Zhang, X. Zheng, X. Sun, K. Fu, and H. Wang, “A new method on inshore ship detection in high-resolution satellite images using shape and context information” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 3, March 2014
[4] Z. Shi, X. Yu, Z. Jiang, and B. Li, “Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature”, IEEE Transaction on Geoscience and Remote Sensing, vol. 52, issue 8, Aug 2014 [online]. Available: http://levir.buaa.edu.cn/publications/TGRS-shipDetectionRXHOG.pdf. [Accessed: 22-Mar-2017].
[5] X. Leng, K. Ji, K. Yang, and H. Zou, “A Bilateral CFAR Algorithm for Ship Detection in SAR Images” IEEE Geoscience and Remote Sensing, vol. 12, issue 7, July. 2015.
[6] J. Xu, X. Sun, D. Zhang, and K. Fu, “Automatic Detection of Inshore Ships in High-Resolution Remote Sensing Images Using Robust Invariant Generalized Hough Transform”, IEEE Transaction on Geoscience and remote sensing, vol. 11, no. 12, Dec. 2014.
[7] Y. Liu, M. Zhang, and Z. Guo, “SAR Ship Detection Using Sea-Land Segmentation-Based Convolutional Neural Network”, IEEE International Workshop on Remote Sensing with Intelligent Processing, June 2017.
[8] J. Frost, T. Geisler, and A. Mahajan, “Monitoring Illegal Fishing through Image Classification”, Stanford.edu [Online]. Available: http://cs229.stanford.edu/proj2016/report/FrostGeislerMahajan-MonitoringIllegalFishingThroughImageClassification-report.pdf. [Accessed: 15-june-2017].
[9] I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning”, MIT Press, 2016 [Online]. Available: www.deeplearningbook.org [Accessed: 12-Sep-2016].
[10] E.R. Davies, “Deep Learning”, Computer Vision 5th Edition, page: 453-493, Elsevier.
[11] S.W. Eric, J.K. Betty, G.C. Mark, and N. Diane, “A Random Forest Approach to Predict the Spatial Distribution of Sediment Pollution in an Estuarine System”, journals.plos.org, 2017 [Online]. Available: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179473 [Accessed: 12-Jul-2018].
[12] W. Chen, X. Xie, J. Peng, H. Shahabi, H. Hong, D.T. Bui, Z. Duan, S. Li, and A. Zhu, “GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method”, CATENA, vol. 164, May 2018, pages: 135-149 [Online] Available: https://www.sciencedirect.com/science/article/pii/S0341816218300122 [Accessed: 12-Jul-2018]
DOI: https://doi.org/10.22146/ijccs.37461
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