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.