Ship detection in SAR images based on convolutional neural network

被引：24
|
作者：
Li J. [1 ]
Qu C. [1 ]
Peng S. [1 ]
Deng B. [1 ]
机构：
[1] Naval Aviation University, Yantai
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2018年 / 40卷 / 09期
关键词：
Convolutional neural network; Deep learning; Ship detection; Synthetic aperture radar (SAR);
D O I：
10.3969/j.issn.1001-506X.2018.09.09
中图分类号：
学科分类号：
摘要：
Deep learning has led to impressive performance on a variety of object detection tasks recently. However, it is rarely applied in ship detection of synthetic aperture radar (SAR) images. This paper aims to introduce a detector based on deep learning into this field. We analyze the advantages of the state-of-the-art Faster R-CNN detector in computer vision and limitations in our specific domain. Given this analysis, we propose a dataset and four strategies to improve the detection result. The dataset contains ships in various environments, such as image resolution, ship size, sea condition, and sensor type. It can be a benchmark for researchers to evaluate their algorithms. The strategies include feature concatenation, transfer learning, loss function optimization method, and other implementation details. We conduct some comparison and ablation experiments on our dataset. The result shows that our proposed method obtains better accuracy and higher efficiency. © 2018, Editorial Office of Systems Engineering and Electronics. All right reserved.
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收藏
页码：1953 / 1959
页数：6
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