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- 2015
基于多层反卷积网络的SAR图像分类
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Abstract:
针对传统特征提取方法不能提取目标高层结构特征的问题,提出了一种基于软概率的池化方法,结合多层反卷积网络,学习目标的高层结构特征,并将其用于合成孔径雷达(SAR)图像分类。首先对SAR图像进行子块划分,然后对每个子块进行基于多层反卷积网络的特征编码,学习出不同层次上的图像特征,最后将该特征用于支持向量机(SVM)分类器,实现SAR图像的分类。在国内首批SAR数据上的实验表明,该算法获得了较高的分类准确率
| [1] | Wan Peng, Wang Jianguo, Huang Shunji. A Synthesis Method for SAR Image Target Detection[J]. <em>Acta Electronica Sinica</em>, 2001, 29(3):323-325(万朋, 王建国, 黄顺吉. SAR图像目标综合检测方法[J].电子学报, 2001, 29(3): 323-325) |
| [2] | He Chu, Liu Ming, Xu Lianyu,et al. A Hierarchical Classification Method Based on Feature Selection and Adaptive Decision Tree for SAR Image[J].<em>Geomatics and Information Science of Wuhan University</em>, 2012, 37(1): 46-49 (何楚, 刘明, 许连玉,等. 利用特征选择自适应决策树的层次SAR图像分类[J]. 武汉大学学报·信息科学版, 2012,37(1):46-49) |
| [3] | Hinton G E, Salakhutdinov R. Reducing the Dimensionality of Data with Neural Networks[J]. <em>Science</em>, 2006, 313(5 786):504-507 |
| [4] | Huan Ruohong, Zhang Ping, Pan Yun. SAR Target Recognition Using PCA, ICA and Gabor Wavelet Decision Fusion[J]. <em>Journal of Remote Sensing</em>, 2012, 16(2): 262-274(宦若虹, 张平, 潘赟. ICA、PCA和Gabor小波决策融合的SAR目标识别[J]. 遥感学报, 2012, 16(2): 262-274) |
| [5] | Maitre H. Synthetic Aperture Radar Image Processing[M]. Sun Hong. Beijing: Publishing House of Electronics Industry, 2005 (Maitre H. 合成孔径雷达图像处理[M]. 孙洪.北京: 电子工业出版社, 2005) |
| [6] | Vincent P, Larochelle H, Lajoie I, et al.Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion[J].<em>Journal of Machine Learning Research</em>, 2010,11:3 371-3 408 |
| [7] | Wu Xiaohong, Xie Ming, Gan Ke,et al. Feature Extraction and Target Recognition of SAR Images[J]. <em>Journal of Sichuan University(Natural Science Edition)</em>, 2007, 44(6): 1 275-1 280(吴晓红,谢明, 干可,等. SAR图像的特征提取与目标识别[J].四川大学学报(自然科学版), 2007, 44(6):1 275-1 280) |
| [8] | He Chu, Liu Ming,Feng Qian, et al. PolInSAR Image Classification Based on Compressed Sensing and Multi-scale Pyramid[J]. <em>Acta Automatic Sinica</em>, 2011, 37(7): 820-827(何楚, 刘明, 冯倩,等. 基于多尺度压缩感知金字塔的极化干涉SAR图像分类[J]. 自动化学报, 2011, 37(7): 820-827) |
| [9] | Zhang Xiaolei, Wu Ji. Deep Belief Networks Based Voice Activity Detection[J]. <em>IEEE Transactions on Audio, Speech and Language Processing</em>,2013,21(4):697-710 |
| [10] | Yang J C, Yu K, Gong Y H,et al. Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009 |
| [11] | Zeiler M D, Taylor G W, Fergus R. Adaptive Deconvolutional Networks for Mid and High Level Feature Learning[C].IEEE International Conference on Computer Vision (ICCV), Barcelona,Spain,2011 |
| [12] | Yin Hui. Research on Urban Scene Classification Method Using High Resolution Synthetic Aperture Radar Image Based on Local Feature Representation(殷慧. 基于局部特征表达的高分辨率SAR图像城区场景分类方法研究[D]. 武汉: 武汉大学,2010) |
