基于卷积神经网络的SAR图像目标检测算法

杜兰; 刘彬; 王燕; 刘宏伟; 代慧

doi:10.11999/JEIT161032

基于卷积神经网络的SAR图像目标检测算法

doi: 10.11999/JEIT161032

杜兰^1, ,,
刘彬¹,
王燕¹,
刘宏伟¹,
代慧¹

基金项目:

国家自然科学基金(61271024, 61322103, 61525105)，高等学校博士学科点专项科研基金博导类基金(20130203110013)，陕西省自然科学基金(2015JZ016)

计量
- 文章访问数: 2367
- HTML全文浏览量: 304
- PDF下载量: 979
- 被引次数: 0
出版历程
- 收稿日期: 2016-10-08
- 修回日期: 2016-11-24
- 刊出日期: 2016-12-19

Target Detection Method Based on Convolutional Neural Network for SAR Image

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)

摘要

摘要: 该文研究了训练样本不足的情况下利用卷积神经网络(Convolutional Neural Network, CNN)对合成孔径雷达(SAR)图像实现目标检测的问题。利用已有的完备数据集来辅助场景复杂且训练样本不足的数据集进行检测。首先用已有的完备数据集训练得到CNN分类模型，用于对候选区域提取网络和目标检测网络做参数初始化；然后利用完备数据集对训练数据集做扩充；最后通过四步训练法得到候选区域提取模型和目标检测模型。实测数据的实验结果证明，所提方法在SAR图像目标检测中可以获得较好的检测效果。
- 合成孔径雷达 /
- 目标检测 /
- 卷积神经网络 /
- 训练数据扩充
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

HTML全文

参考文献(17)

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 [C]. The 2nd Asian-Pacific Conference on Synthetic Aperture Radar, Xian, China, 2009: 506-509.

LENG X, JI K, YANG K, et al. A bilateral CFAR algorithm for ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1536-1540. doi: 10.1109 /LGRS.2015.2412174.

JI Y, ZHANG J, MENG J, et al. A new CFAR ship target detection method in SAR imagery[J]. Acta Oceanologica Sinica, 2010, 29(1): 12-16.

ELDHUSET K. An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4): 1010-1019. doi: 10.1109/36.508418.

KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012: 1097-1105.

SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA, 2015: 1-9.

ZHANG X, ZOU J, MING X, et al. Efficient and accurate approximations of nonlinear convolutional networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, Massachusetts, USA, 2015: 1984-1992.

HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]. Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, 2016: 770-778.

ZEILER M D and FERGUS R. Visualizing and understanding convolutional networks[C]. European Conference on Computer Vision. Springer International Publishing, Zurich, Swizterland, 2014: 818-833.

GOODFELLOW I J, WARDE Farley D, MIRZA M, et al. Maxout networks[J]. International Conference on Machine Learning, 2013, 28(3): 1319-1327.

HE K, ZHANG X, REN S, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]. International Conference on Computer Vision, Santiago, Chile, 2015: 1026-1034.

GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, USA, 2014: 580-587.

GIRSHICK R. Fast R-CNN[C]. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440-1448.

REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]. Advances in Neural Information Processing Systems, Montral, Canada, 2015: 91-99.

IOFFE S and SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]. International Conference on Machine Learning, Lille, France, 2015: 448-456.

LONG J, SHELHAMER E, and DARRELL T. Fully convolutional networks for semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA, 2015: 3431-3440.

GAO G, LIU L, ZHAO L, et al. An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1685-1697. doi: 10.1109/TGRS.2008.2006504.

施引文献

资源附件(0)

访问统计