基于复值卷积神经网络样本精选的极化SAR图像弱监督分类方法

秦先祥; 余旺盛; 王鹏; 陈天平; 邹焕新

doi:10.12000/JR20062

基于复值卷积神经网络样本精选的极化SAR图像弱监督分类方法

DOI: 10.12000/JR20062

秦先祥^1, ,,
余旺盛^1,,
王鹏^1,,
陈天平^1,,
邹焕新^2,

1.
空军工程大学信息与导航学院西安 710077
2.
国防科技大学电子科学学院长沙 410073

基金项目: 国家自然科学基金(41601436, 61403414, 61703423)，陕西省自然科学基础研究计划(2018JM4029)

详细信息

作者简介:
秦先祥(1986–)，男，广西人，空军工程大学信息与导航学院讲师，主要研究方向为SAR图像智能处理与分析。E-mail: qinxianxiang@126.com

余旺盛(1985–)，男，湖南人，空军工程大学信息与导航学院讲师，主要研究方向为计算机视觉与图像处理。E-mail: xing_fu_yu@sina.com

王　鹏(1985–)，男，山西人，空军工程大学信息与导航学院副教授，硕士生导师，主要研究方向为信息融合处理与分布式协同控制。E-mail: blueking1985@hotmail.com

陈天平(1979–)，男，四川人，空军工程大学信息与导航学院讲师，主要研究方向为智能信息处理技术。E-mail: chentianping1979@163.com

邹焕新(1973–)，男，广东人，国防科技大学电子科学学院教授，硕士生导师，主要研究方向为SAR图像解译、多源信息融合、计算机视觉、图像处理、模式识别等。E-mail: hxzou2008@163.com

通讯作者:
秦先祥 qinxianxiang@126.com

责任主编：王爽 Corresponding Editor: WANG Shuang
中图分类号: TN958
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出版历程
- 收稿日期: 2020-05-13
- 修回日期: 2020-06-26
- 网络出版日期: 2020-06-01

Weakly Supervised Classification of PolSAR Images Based on Sample Refinement with Complex-Valued Convolutional Neural Network

1.
Information and Navigation College, Air Force Engineering University, Xi’an 710077, China
2.
College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (41601436, 61403414, 61703423), The Natural Science Basic Research Plan in Shaanxi Province (2018JM4029)

More Information

Corresponding author: QIN Xianxiang, qinxianxiang@126.com

摘要

摘要:
针对物体框标注样本包含大量异质成分的问题，该文提出了一种基于复值卷积神经网络(CV-CNN)样本精选的极化SAR(PolSAR)图像弱监督分类方法。该方法首先采用CV-CNN对物体框标注样本进行迭代精选，并同时训练出可直接用于分类的CV-CNN。然后利用所训练的CV-CNN完成极化SAR图像的分类。基于3幅实测极化SAR图像的实验结果表明，该文方法能够有效剔除异质样本，与采用原始物体框标注样本的传统全监督分类方法相比可以获得明显更优的分类结果，并且该方法采用CV-CNN比采用经典的支持矢量机(SVM)或Wishart分类器性能更优。
- 极化SAR /
- 弱监督分类 /
- 复值卷积神经网络 /
- 样本精选
Abstract:
In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that the bounding-box labeled samples contain many heterogeneous components. First, CV-CNN is used for iteratively refining the bounding-box labeled samples, and the CV-CNN that can be used for direct classification is trained simultaneously. Then, the given PolSAR image is classified using the trained CV-CNN. The experimental results obtained using three actual PolSAR images demonstrate that the heterogeneous components can be effectively eliminated using the proposed method, obtaining significantly better classification results when compared with those obtained using the traditional fully supervised classification method in which original bounding-box labeled samples are used. Furthermore, the proposed method with CV-CNN is superior to those in which the classical Support Vector Machine(SVM) and Wishart classifier are used.
- PolSAR /
- Weakly supervised classification /
- Complex-Valued Convolutional Neural Network(CV-CNN) /
- Sample refinement

HTML全文

图 1 极化SAR数据样本的像素级标注与物体框标注对比示意图

Figure 1. Comparison illustration of pixel-level label and bounding-box label for a PolSAR data sample

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图 2 物体框标注样本精选方法流程图

Figure 2. Flowchart of refining method for bounding-box labelled samples

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图 3 CV-CNN的结构示意图

Figure 3. Illustration of architecture of CV-CNN

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图 4 实验图像数据1

Figure 4. Experimental image data 1

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图 5 实验图像数据2

Figure 5. Experimental image data 2

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图 6 实验图像数据3

Figure 6. Experimental image data 3

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图 7 实验数据1的物体框标注样本集的Pauli-RGB图像及3种方法所得分类结果和精选像素级标签

Figure 7. Pauli-RGB image of the bounding-box labelled sample set of experimental data 1 and its classification results and refined pixel-level labels with three methods

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图 8 实验数据2的物体框标注样本集的Pauli-RGB图像及3种方法所得分类结果和精选像素级标签

Figure 8. Pauli-RGB image of the bounding-box labelled sample set of experimental data 2 and its classification results and refined pixel-level labels with three methods

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图 9 实验数据3的物体框标注样本集的Pauli-RGB图像及3种方法所得分类结果和精选像素级标签

Figure 9. Pauli-RGB image of the bounding-box labelled sample set of experimental data 3 and its classification results and refined pixel-level labels with three methods

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图 10 实验数据1训练样本集的分类结果变化率曲线

Figure 10. Curves of change rate of classification results on training set of experimental data 1

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图 11 实验数据1的全监督和弱监督分类结果

Figure 11. Classification results of experimental data 1 by fully-supervised and proposed weakly-supervised methods

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图 12 实验数据2的全监督和弱监督分类结果

Figure 12. Classification results of experimental data 2 by fully-supervised and proposed weakly-supervised methods

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图 13 实验数据3的全监督和弱监督分类结果

Figure 13. Classification results of experimental data 3 by fully-supervised and proposed weakly-supervised methods

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表 1 实验数据1的分类精度(%)、总体精度(%)和Kappa系数

Table 1. Classification accuracy (%), overall accuracy (%) and Kappa coefficient for experimental data 1

方法	蚕豆	豌豆	树林	苜蓿	小麦1	甜菜	土豆	裸地	草地
CV-CNN全监督	56.43	89.02	99.21	20.37	97.12	80.63	49.86	100.00	30.96
CV-CNN弱监督	56.14	98.35	85.18	92.72	88.23	89.00	70.87	100.00	85.56
Wishart全监督	56.51	81.19	88.15	39.88	54.74	35.49	67.11	0.68	0.11
Wishart弱监督	61.63	80.52	81.46	85.90	73.71	91.21	63.84	99.71	62.02
SVM全监督	85.43	74.37	71.80	67.52	68.37	52.59	78.21	21.05	0
SVM弱监督	81.77	73.20	68.44	58.26	61.42	55.45	75.99	27.18	0

方法	油菜籽	大麦	小麦2	小麦3	水域	建筑区	总体精度	Kappa系数
CV-CNN全监督	48.35	95.74	94.36	91.29	90.41	96.22	76.87	0.7473
CV-CNN弱监督	48.21	93.66	95.69	89.81	81.69	99.37	84.58	0.8323
Wishart全监督	19.48	97.48	76.32	53.16	80.51	91.81	58.02	0.5440
Wishart弱监督	44.47	87.02	67.36	68.58	37.47	90.13	69.38	0.6674
SVM全监督	31.42	29.47	39.90	75.97	77.51	67.75	57.09	0.5352
SVM弱监督	36.51	34.76	39.51	70.24	76.72	70.27	56.58	0.5291

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表 2 实验数据2的分类精度(%)、总体精度(%)和Kappa系数

Table 2. Classification accuracy (%), overall accuracy (%) and Kappa coefficient for experimental data 2

方法	农田	植被	水域	建筑区	总体精度	Kappa系数
CV-CNN全监督	82.42	90.41	98.79	64.91	82.14	0.7458
CV-CNN弱监督	93.34	91.38	98.76	75.88	90.02	0.8537
Wishart全监督	99.76	55.95	0.05	13.07	36.36	0.1515
Wishart弱监督	99.76	34.09	0.01	20.22	34.54	0.1272
SVM全监督	84.33	59.42	71.18	52.22	73.52	0.6073
SVM弱监督	88.07	52.05	93.66	38.79	70.40	0.5797

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表 3 实验数据3的分类精度(%)、总体精度(%)和Kappa系数

Table 3. Classification accuracy (%), overall accuracy (%) and Kappa coefficient for experimental data 3

方法	水域	植被	城区A	城区B	城区C	总体精度	Kappa系数
CV-CNN全监督	99.37	82.37	7.55	88.89	91.57	74.56	0.6466
CV-CNN弱监督	99.43	91.98	81.84	80.09	82.84	91.05	0.8731
Wishart全监督	95.58	63.34	23.14	33.05	32.03	45.76	0.3219
Wishart弱监督	86.68	61.32	51.52	24.99	31.77	45.92	0.3239
SVM全监督	85.53	26.71	56.13	23.44	38.99	54.34	0.3802
SVM弱监督	88.51	27.71	56.55	27.44	35.86	54.87	0.3940

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