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9 September 2019 Ship Target Detection Based on CFAR and Deep Learning SAR Image
Hua Deng, Dechang Pi, Yue Zhao
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Abstract

Deng, H.; Pi, D.-C., and Zhao, Y., 2019. Ship target detection based on CFAR and deep learning SAR image. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 161–164. Coconut Creek (Florida), ISSN 0749-0208.

Ship activity has become more and more frequent with the development of maritime transportation. Therefore, the rapid and accurate positioning performance of marine vessels is becoming more and more important. Because SAR images have the characteristics of all-weather detection, the analysis of SAR images becomes a marine ship. An important method of detection, but the existing ship detection methods have the disadvantages of low precision and slow speed. In order to solve this problem, this paper proposes a method based on deep learning to realize the fast and accurate automatic detection of marine ships through RadarSat -2 data is simulated. The results show that the proposed method effectively describes the ship's target characteristics and the accuracy is greatly improved.

©Coastal Education and Research Foundation, Inc. 2019
Hua Deng, Dechang Pi, and Yue Zhao "Ship Target Detection Based on CFAR and Deep Learning SAR Image," Journal of Coastal Research 94(sp1), 161-164, (9 September 2019). https://doi.org/10.2112/SI94-033.1
Received: 26 January 2019; Accepted: 1 April 2019; Published: 9 September 2019
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KEYWORDS
Constant False Alarm Rate (CFAR)
deep learning
ship detection
Synthetic Aperture Radar (SAR)
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