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| Target Detection Method Based on Convolutional Neural Network for SAR Image |
| DU Lan LIU Bin WANG Yan LIU Hongwei DAI Hui |
(National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China)
(Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, Xi’an 710071, China) |
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Abstract This paper studies the issue of SAR target detection with CNN when the training samples are insufficient. The existing complete dataset is employed to assist accomplishing target detection task, where the training samples are not enough and the scene is complicated. Firstly, the existing complete dataset with image-level annotations is used to pre-train a CNN classification model, which is utilized to initialize the region proposal network and detection network. Then, the training dataset is enlarged with the existing complete dataset. Finally, the region proposal model and detection model are obtained through the pragmatic “4-step training algorithm” with the augmented training dataset. The experimental results on the measured data demonstrate that the proposed method can improve the detection performance compared with the traditional detection methods.
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Received: 08 October 2016
Published: 14 December 2016
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| Fund: The National Natural Science Foundation of China (61271024, 61322103, 61525105), The Foundation for Doctoral Supervisor of China (20130203110013), The Natural Science Foundation of Shaanxi Province (2015JZ016) |
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Corresponding Authors:
DU Lan
E-mail: dulan@mail.xidian.edu.cn
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