@inproceedings{8f4a869c1d644a2fa4528afc7f538c30,
title = "SAR ship detection based on resnet and transfer learning",
abstract = "Synthetic Aperture Radar (SAR) ship detection has been a research hotspot and is significant for marine surveillance. Traditional constant false alarm rate (CFAR) detector has the disadvantages of high false alarm and poor adaptability. Deep learning provides a unique solution for SAR ship detection. However, the traditional deep network cannot reach very deep thus the accuracy is limited, and the training speed is slow. In this paper, a very deep network ResNet with higher accuracy and faster training speed is applied to train the SAR ship detection model. Moreover, transfer learning is applied to combat the small dataset. The proposed method is tested on a general SAR ship dataset and achieves 94.7\% average precision. Comparative experiments show that our method has the best performance and which verifies the effectiveness of our method.",
keywords = "Deep learning, ResNet, SAR, Ship detection, Transfer learning",
author = "Yong Li and Zegang Ding and Chi Zhang and Yan Wang and Jing Chen",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
doi = "10.1109/IGARSS.2019.8900290",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1188--1191",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "United States",
}