Target detection method based on convolutional neural network for SAR image

被引：0

作者：

Du L. ^{[1
,2
]}

Liu B. ^{[1
,2
]}

Wang Y. ^{[1
,2
]}

Liu H. ^{[1
,2
]}

Dai H. ^{[1
,2
]}

机构：

[1] National Laboratory of Radar Signal Processing, Xidian University, Xi'an

[2] Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, Xi'an

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2016年 / 38卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Convolutional Neural Network (CNN); SAR; Target detection; Training data augmentation;

D O I：

10.11999/JEIT161032

中图分类号：

学科分类号：

摘要：

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. © 2016, Science Press. All right reserved.

引用

页码：3018 / 3025

页数：7

共 17 条

[1]

Xing X.W., Chen Z.L., Zou H.X., Et al., A fast algorithm based on two-stage CFAR for detecting ships in SAR images, The 2nd Asian-Pacific Conference on Synthetic Aperture Radar, pp. 506-509, (2009)

[2]

Leng X., Ji K., Yang K., Et al., A bilateral CFAR algorithm for ship detection in SAR images, IEEE Geoscience and Remote Sensing Letters, 12, 7, pp. 1536-1540, (2015)

[3]

Ji Y., Zhang J., Meng J., Et al., A new CFAR ship target detection method in SAR imagery, Acta Oceanologica Sinica, 29, 1, pp. 12-16, (2010)

[4]

Eldhuset K., An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions, IEEE Transactions on Geoscience and Remote Sensing, 34, 4, pp. 1010-1019, (1996)

[5]

Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, pp. 1097-1105, (2012)

[6]

Szegedy C., Liu W., Jia Y., Et al., Going deeper with convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, (2015)

[7]

Zhang X., Zou J., Ming X., Et al., Efficient and accurate approximations of nonlinear convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1984-1992, (2015)

[8]

He K., Zhang X., Ren S., Et al., Deep residual learning for image recognition, Computer Vision and Pattern Recognition, pp. 770-778, (2016)

[9]

Zeiler M.D., Fergus R., Visualizing and understanding convolutional networks, European Conference on Computer Vision, pp. 818-833, (2014)

[10]

Goodfellow I.J., Warde F.D., Mirza M., Et al., Maxout networks, International Conference on Machine Learning, 28, 3, pp. 1319-1327, (2013)

← 1 2 →