Target Detection Method Based on Convolutional Neural Network for SAR Image

DU Lan; LIU Bin; WANG Yan; LIU Hongwei; DAI Hui

doi:10.11999/JEIT161032

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 3018-3025

DU Lan, LIU Bin, WANG Yan, LIU Hongwei, DAI Hui. Target Detection Method Based on Convolutional Neural Network for SAR Image[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3018-3025. doi: 10.11999/JEIT161032

Citation:

DU Lan, LIU Bin, WANG Yan, LIU Hongwei, DAI Hui. Target Detection Method Based on Convolutional Neural Network for SAR Image[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3018-3025. doi: 10.11999/JEIT161032

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Target Detection Method Based on Convolutional Neural Network for SAR Image

doi: 10.11999/JEIT161032

DU Lan^{1
,
,},
LIU Bin¹,
WANG Yan¹,
LIU Hongwei¹,
DAI Hui¹

Funds:

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)

Received Date: 2016-10-08
Rev Recd Date: 2016-11-24
Publish Date: 2016-12-19

Abstract

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.
- SAR,
- Target detection,
- Convolutional Neural Network (CNN),
- Training data augmentation

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References(17)

References

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