Volume 6 Issue 5
Oct.  2017
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Article Navigation >  Journal of Radars  > 2017  >  6(5): 514-523
Zhao Juanping, Guo Weiwei, Liu Bin, Cui Shiyong, Zhang Zenghui, Yu Wenxian. Convolutional Neural Network-based SAR Image Classification with Noisy Labels[J]. Journal of Radars, 2017, 6(5): 514-523. doi: 10.12000/JR16140
Citation: Zhao Juanping, Guo Weiwei, Liu Bin, Cui Shiyong, Zhang Zenghui, Yu Wenxian. Convolutional Neural Network-based SAR Image Classification with Noisy Labels[J]. Journal of Radars, 2017, 6(5): 514-523. doi: 10.12000/JR16140
shu

Convolutional Neural Network-based SAR Image Classification with Noisy Labels

doi: 10.12000/JR16140
  • ①.

    Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China

  • ②.

    Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling 82234, Germany

Funds:  The National Natural Science Foundation of China (61331015), The China Postdoctoral Science Foundation (2015M581618)
  • Received Date: 2016-12-06
  • Rev Recd Date: 2017-04-07
    • Available Online: 2017-04-21
  • Publish Date: 2017-10-28
    Abstract
  • SAR image classification is an important task in SAR image interpretation. Supervised learning methods, such as the Convolutional Neural Network (CNN), demand samples that are accurately labeled. However, this presents a major challenge in SAR image labeling. Due to their unique imaging mechanism, SAR images are seriously affected by speckle, geometric distortion, and incomplete structural information. Thus, SAR images have a strong non-intuitive property, which causes difficulties in SAR image labeling, and which results in the weakened learning and generalization performance of many classifiers (including CNN). In this paper, we propose a Probability Transition CNN (PTCNN) for patch-level SAR image classification with noisy labels. Based on the classical CNN, PTCNN builds a bridge between noise-free labels and their noisy versions via a noisy-label transition layer. As such, we derive a new CNN model trained with a noisily labeled training dataset that can potentially revise noisy labels and improve learning capacity with noisily labeled data. We use a 16-class land cover dataset and the MSTAR dataset to demonstrate the effectiveness of our model. Our experimental results show the PTCNN model to be robust with respect to label noise and demonstrate its promising classification performance compared with the classical CNN model. Therefore, the proposed PTCNN model could lower the standards required regarding the quality of image labels and have a variety of practical applications.

     

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    • References(24)
  • [1]
    Krizhevsky A, Sutskever I, and Hinton G E. Imagenet classification with deep convolutional neural networks[C]. Advances in Neural Information Processing Systems, 2012: 1097–1105.
    [2]
    He K, Zhang X, Ren S, et al.. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904–1916. doi: 10.1109/TPAMI.2015.2389824
    [3]
    Chan T H, Jia K, Gao S, et al.. PCANet: A simple deep learning baseline for image classification?[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5017–5032. doi: 10.1109/TIP.2015.2475625
    [4]
    Chen X, Xiang S, Liu C L, et al.. Vehicle detection in satellite images by hybrid deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1797–1801. doi: 10.1109/LGRS.2014.2309695
    [5]
    Kalchbrenner N, Grefenstette E, and Blunsom P. A convolutional neural network for modelling sentences[J]. arXiv Preprint arXiv: 1404. 2188, 2014.
    [6]
    Kim Y. Convolutional neural networks for sentence classification[J]. arXiv Preprint arXiv: 1408. 5882, 2014.
    [7]
    Chen S and Wang H. SAR target recognition based on deep learning[C]. 2014 International Conference on Data Science and Advanced Analytics (DSAA), Shanghai, 2014: 541–547.
    [8]
    Wagner S. Combination of convolutional feature extraction and support vector machines for radar ATR[C]. 17th International Conference on Information Fusion (FUSION), Salamanca, 2014: 1–6.
    [9]
    田壮壮, 占荣辉, 胡杰民, 等. 基于卷积神经网络的SAR图像目标识别研究[J]. 雷达学报, 2016, 5(3): 320–325. http://radars.ie.ac.cn/CN/abstract/abstract351.shtml

    Tian Zhuangzhuang, Zhan Ronghui, Hu Jiemin, et al.. SAR ATR based on convolutional neural networks[J]. Journal of Radars, 2016, 5(3): 320–325. http://radars.ie.ac.cn/CN/abstract/abstract351.shtml
    [10]
    Li X, Li C, Wang P, et al.. SAR ATR based on dividing CNN into CAE and SNN[C]. 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore, 2015: 676–679.
    [11]
    Ding J, Chen B, Liu H, et al.. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368.
    [12]
    Zhao J, Guo W, Cui S, et al.. Convolutional neural network for SAR image classification at patch level[C]. International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016: 945–948.
    [13]
    Chen S, Wang H, Xu F, et al.. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720
    [14]
    Deng J, Dong W, Socher R, et al.. Imagenet: A large-scale hierarchical image database[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami Beach, Florida, 2009: 248–255.
    [15]
    Zhu X and Wu X. Class noise vs. attribute noise: A quantitative study[J]. Artificial Intelligence Review, 2004, 22(3): 177–210. doi: 10.1007/s10462-004-0751-8
    [16]
    Chang C C and Lin C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 27.
    [17]
    Jia Y, Shelhamer E, Donahue J, et al.. Caffe: Convolutional architecture for fast feature embedding[C]. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, 2014: 675–678.
    [18]
    Hecht-Nielsen R. Theory of the backpropagation neural network[C]. IEEE International Joint Conference on Neural Networks, 1989: 593–605.
    [19]
    Bottou L. Stochastic gradient learning in neural networks[J]. Proceedings of Neuro-Nımes, 1991, 91(8).
    [20]
    Cui S, Dumitru C O, and Datcu M. Semantic annotation in earth observation based on active learning[J]. International Journal of Image and Data Fusion, 2014, 5(2): 152–174. doi: 10.1080/19479832.2013.858778
    [21]
    Popescu A A, Gavat I, and Datcu M. Contextual descriptors for scene classes in very high resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(1): 80–84. doi: 10.1109/LGRS.2011.2160838
    [22]
    Singh J, Cui S, Datcu M, et al.. A survey of density estimation for SAR images[C]. 20th European of Signal Processing Conference (EUSIPCO), 2012: 2526–2530.
    [23]
    Ross T D, Worrell S W, Velten V J, et al.. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. Aerospace/Defense Sensing and Controls. International Society for Optics and Photonics, 1998: 566–573.
    [24]
    Wu T, Chen X, Ruang X W, et al.. Study on SAR target recognition based on support vector machine[C]. 2nd Asian-Pacific Conference on Synthetic Aperture Radar, 2009: 856–859.
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