基于概率转移卷积神经网络的含噪标记SAR图像分类

赵娟萍; 郭炜炜; 柳彬; 崔世勇; 张增辉; 郁文贤

doi:10.12000/JR16140

基于概率转移卷积神经网络的含噪标记SAR图像分类

DOI: 10.12000/JR16140

赵娟萍^1,,
郭炜炜^1,,
柳彬^1,,
崔世勇^2,,
张增辉^1, ,,
郁文贤^1,

①.
上海交通大学智能探测与识别上海市高校重点实验室上海 200240
②.
德国宇航局遥感技术研究所德国韦斯灵 82234

基金项目: 国家自然科学基金重点项目(61331015)，中国博士后基金项目(2015M581618)

详细信息

作者简介:
郭炜炜(1983–)，男，江苏南通人，博士，分别于2005年、2007年和2011年获国防科技大学信息与通信工程专业学士、硕士和博士学位。2014年至今，在上海交通大学电子信息与电气工程学院做博士后。主要从事图像理解、模式识别与机器学习等方面的研究。E-mail: gwnudt@163.com

柳　彬(1985–)，男，湖南衡阳人，博士，助理研究员，分别于2007年、2009年和2015年获上海交通大学信息工程、信号与信息处理和信号与信息处理学士、硕士和博士学位。2012年10月至2013年4月在法国巴黎高科电信学院访问研究。2015年12月，任上海交通大学电信学院信息技术与电气工程研究院助理研究员。主要从事雷达图像的分割分类、目标检测识别、多时相分析等方面的研究。E-mail: bliu.rsti@sjtu.edu.cn

通讯作者:
张增辉 zenghui.zhang@sjtu.edu.cn

中图分类号: TN957.52
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出版历程
- 收稿日期: 2016-12-06
- 修回日期: 2017-04-07
- 网络出版日期: 2017-10-28

Convolutional Neural Network-based SAR Image Classification with Noisy Labels

Zhao Juanping^1
,,
Guo Weiwei^1
,,
Liu Bin^1
,,
Cui Shiyong^2
,,
Zhang Zenghui^{1
, ,},
Yu Wenxian^1
,

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)

摘要

摘要: 合成孔径雷达(Synthetic Aperture Radar, SAR)图像分类是SAR图像解译的重要任务。以卷积神经网络(Convolutional Neural Networks, CNN)为代表的监督学习方法需要大量已标注的训练样本。然而对于SAR图像真值标注而言，由于SAR特殊的成像机理，图像受相干斑噪声、几何畸变和结构缺失等因素影响较为严重，非直观性较强，使得SAR图像人工标注非常困难，极易出错，从而导致CNN等模型学习和泛化性能急剧降低。针对这种含噪标记条件下的SAR图像分类问题，该文提出了一种基于概率转移模型的卷积神经网络(Probability Transition CNN, PTCNN)方法，该方法在传统CNN模型基础上，基于含噪标记与正确标记之间的概率转移模型，建立噪声标记转移层，这种新的卷积网络模型可潜在地校正错误标记，增强了含噪标记下分类模型的鲁棒性。与经典CNN等模型相比，在构建的16类SAR图像地物数据集和MSTAR数据集上的实验结果表明该文方法相比于经典CNN等模型，在保持SAR图像分类性能的同时具有较好的抗噪性，能够有效校正训练样本中的标注错误，从而降低了SAR图像有监督分类任务对样本标注质量的要求，具有一定的研究价值与应用前景。
- 合成孔径雷达(SAR)图像分类 /
- 监督学习 /
- 含噪标记 /
- 概率转移卷积神经网络(PTCNN) /
- 深度特征
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.
- SAR image classification /
- Supervised learning /
- Noisy labels /
- Probability Transition Convolutional Neural Network (PTCNN) /
- Deep features

HTML全文

图 1 概率转移卷积神经网络

Figure 1. Probability transition convolutional neural network

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图 2 SAR图像含噪标记模型

Figure 2. Model of SAR image noisy labels

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图 3 深度特征提取网络结构

Figure 3. Deep feature extraction structure

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图 4 分类准确率随噪声比例变化曲线

Figure 4. Classification accuracy varies with noise fraction

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图 5 类别噪声比例为30%时3种方法的测试混淆矩阵

Figure 5. Confusion matrix of three method with 30% noise fraction

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图 6 不同标记噪声比例下3种方法的特征分布

Figure 6. Feature distribution of three method with different noise label fractions

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表 1 TerraSAR-X卫星成像参数

Table 1. TerraSAR-X satellite imaging parameters

参数	武汉地区	上海交大闵行校区
产品类型	MGD Level 1b	SSC Level 1b
成像模式	Spotlight	Staring spotlight
图像尺寸/地距向(pixel)×方位向(pixel)	7500×8000	18299×7880
像元大小/地距向(m)×方位向(m)	1.60×1.27	0.71×0.17

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表 2 SAR图像地物分类训练与测试数据集

Table 2. Training and testing set for SAR image land cover classification

地物分类数据集	训练集	测试集
港口	112	76
稀疏建筑区	247	169
池塘	168	112
桥梁	180	120
湿地	233	155
舰船	146	98
小溪	264	176
公路	91	61
运动场	127	85
沙滩	247	165
密集建筑区	274	182
河流	101	67
森林	62	42
道路	192	128
绿化带	461	307
水体	293	195
总计	3198	2138

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表 3 用于训练和测试的MSTAR数据集

Table 3. Training and testing set of MSTAR database

MSTAR数据集	训练集	测试集
2S1	299	274
BMP2_C21	233	196
BRDM_2	298	274
BTR_60	256	195
BTR_70	233	196
D7	299	274
T62	299	273
T72_132	232	196
ZIL131	299	274
ZSU_23_4	299	274
总计	2747	2426

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表 4 不同标记噪声比例下的地物分类准确率(%)

Table 4. Land cover classification accuracies of different label noise fraction (%)

噪声比例	PTCNN	CNN	Gabor+SVM
0	98.94	96.62	94.05
10	98.61	93.14	84.35
20	98.80	86.23	78.07
30	98.79	85.22	71.93
40	98.89	76.99	62.93
50	98.61	69.85	52.86

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表 5 不同标记噪声比例下MSTAR车辆目标分类准确率(%)

Table 5. Classification accuracies of MSTAR vehicle target with different noise fractions (%)

噪声比例	PTCNN	CNN	PCA+SVM
0	97.76	96.92	84.67
10	97.00	93.48	71.39
20	96.76	89.48	63.07
30	96.00	84.40	56.80
40	95.43	80.36	51.90
50	95.30	68.68	41.92

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参考文献(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.