Volume 37, Number 3, June 2019
|Page(s)||587 - 593|
|Published online||20 September 2019|
Detection and Recognition of SAR Small Ship Objects Using Deep Neural Network
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2 Shanghai Institute of Satellite Engineering, Shanghai 200240, China
Synthetic aperture radar(SAR) ship target detection plays an increasingly important role in marine monitoring. Aimed at the problems of recognizing small size of ship targets in SAR images and the inability of traditional methods to extract fine target features due to external disturbances, we propose an improved SAR small target detection model based on the deep learning technology. The proposed model mainly consists of two parts:region proposal network(RPN) and object detection network. Firstly, a CNN model is designed and trained to accurately identify small ship targets. Then, the model is used to initialize the parameters of the shared feature extraction layer. Last, we train the proposed object detection model using a self-collected Sentinel-1 SAR small target dataset. The experimental results show that the proposed target detection model has better detection and recognition performance and anti-interference ability for small ship scalable targets in SAR images, and has certain reference value for the research of small target detection in SAR images.
Key words: SAR image / ship target / deep neural network / target detection / feature extraction / candidate region extraction
关键字 : SAR图像 / 舰船目标 / 深度神经网络 / 目标检测 / 特征提取 / 候选区域提取
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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