高分三号SAR影像在国家海域使用动态监测中的应用

范剑超; 王德毅; 赵建华; 宋德瑞; 韩敏; 姜大伟

doi:10.12000/JR17080

高分三号SAR影像在国家海域使用动态监测中的应用

DOI: 10.12000/JR17080

范剑超^1,2, ,,
王德毅^3,,
赵建华^1,2,,
宋德瑞^1,2,,
韩敏^3,,
姜大伟^1,2,

①.
国家海洋环境监测中心海域资源环境动态监管中心大连 116023
②.
国家海洋局海域管理技术重点实验室大连 116023
③.
大连理工大学电子信息与电气工程学部大连 116024

基金项目: 国家重点研发计划(2016YFC1401007, 2017YFC1404902)，国家自然科学基金(41706195, 61273307)，国家高分重大科研专项(41-Y30B12-9001-14/16)

详细信息

作者简介:
范剑超(1985–)，男，内蒙古巴彦淖尔人，博士，副研究员。2012年于大连理工大学电子信息与电气工程学部获得博士学位，现为国家海洋环境监测中心海域动态监管中心副研究员。主要研究方向为SAR图像处理、人工智能等，目前已经发表论文50余篇。E-mail: jcfan@nmemc.org.cn

王德毅(1989–)，男，辽宁鞍山人，大连理工大学电子信息与电气工程学部控制理论与控制工程专业博士研究生，研究方向为遥感影像解译、机器学习。E-mail: deyiwang@mail.dlut.edu.cn

赵建华(1977–)，男，安徽临泉人，博士，工程技术带头人，2004年获浙江大学构造地质学博士学位，现任国家海洋环境监测中心联合工作组首席专家。主要研究方向为海域动态监测业务，目前已经发表论文30余篇。E-mail: jhzhao77@163.com

宋德瑞(1978–)，男，黑龙江通河人，在读博士，高级工程师，2016年辽宁师范大学城市与环境学院博士研究生，现任国家海洋环境监测中心海域动态监管中心高级工程师。主要研究方向为海域动态监测业务信息化，目前已经发表论文20余篇。E-mail: drsong@nmemc.org.cn

韩　敏(1959–)，女，辽宁大连人，博士，教授。1999年于日本国立九州大学获得博士学位，现为大连理工大学电子信息与电气工程学部教授，博士生导师。主要研究方向为神经网络、模式识别、复杂工业系统建模与控制、智能技术及优化算法，目前已发表论文270余篇。E-mail: minhan@dlut.edu.cn

姜大伟(1991–)，男，山东德州人，硕士，助教，2016年在辽宁师范大学城市与环境学院获得硕士学位，现担任通化师范学院历史与地理学院教师。主要研究方向为海洋遥感SAR图像处理。目前已发表论文3篇。E-mail: 18840817436@163.com

通讯作者:
范剑超 jcfan@nmemc.org.cn

中图分类号: TP79
计量
- 文章访问数: 4664
- HTML全文浏览量: 1790
- PDF下载量: 993
- 被引次数: 0
出版历程
- 收稿日期: 2017-09-07
- 修回日期: 2017-10-17
- 网络出版日期: 2017-10-28

National Sea Area Use Dynamic Monitoring Based on GF-3 SAR Imagery

Fan Jianchao^{1,2
, ,},
Wang Deyi^3
,,
Zhao Jianhua^{1,2
,},
Song Derui^{1,2
,},
Han Min^3
,,
Jiang Dawei^{1,2
,}

①.
National Marine Environmental Monitoring Center, Marine Resource Environmental Dynamical Monitoring Center, Dalian 116023, China
②.
Key Laboratory of Sea-Area Management Technology, State Oceanic Administration, Dalian 116023, China
③.
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China

Funds: The National Key R&D Program of China (2016YFC1401007, 2017YFC1404902), The National Natural Science Foundation of China (41706195, 61273307), The National High Resolution Special Research (41-Y30B12-9001-14/16)

摘要

摘要: 高分三号作为我国首颗民用C波段多极化多成像模式SAR卫星，其全天时全天候观测特点，在国家海域使用动态监测中具有较大优势。该文在分析国家海域使用遥感监测的基础上，探讨GF-3号 SAR成像模式和标准预处理方式，并以海岸线围填海、海水养殖等典型海域使用要素为例，给出GF-3不同成像模式在海域使用要素识别分类的部分研究结果，并与现有方法进行对比分析，最后展望了进一步研究方向。
- 合成孔径雷达 /
- 高分三号 /
- 海域使用动态监测 /
- 海岸线 /
- 围填海 /
- 海水养殖
Abstract: GaoFen-3 (GF-3) is the first commercial C-Band multi-polarimetric Synthetic Aperture Radar (SAR) satellite that was launched by China. The characteristics observed by both all-day and all-weather observation depict significant advantages of national sea area use dynamic monitoring. We have thoroughly discussed both the imaging mode and the standard preprocessing of GF-3 imagery by analyzing national sea area use dynamic monitoring. We have portrayed reclamation and aquaculture as significant examples of dynamic monitoring. We have presented both identification and classification results using various image modes of GF-3 satellite, compared with the existing approaches. Finally, we have elaborated on the scope for future research.
- Synthetic Aperture Radar (SAR) /
- GF-3 /
- Marine area use dynamic monitor /
- Coastline /
- Reclamation /
- Floating raft aquaculture

HTML全文

图 1 海域使用动态监测示意图

Figure 1. Schematic diagram of sea-area use dynamic monitoring

下载: 全尺寸图片幻灯片

图 2 GF-3 SAR全国近岸海域数据覆盖情况

Figure 2. GF-3 SAR data coverage of coastal area of China

下载: 全尺寸图片幻灯片

图 3 GF-3 SAR围填海动态监测流程图

Figure 3. Dynamic monitoring process flow chart of reclamation in SAR image

下载: 全尺寸图片幻灯片

图 4 海口地区SAR影像基础原始岸线提取

Figure 4. The basic result of coastline extraction result of Haikou area

下载: 全尺寸图片幻灯片

图 5 根据处理流程进行SAR围填海信息提取

Figure 5. Coastline information extraction according to dynamic monitoring process

下载: 全尺寸图片幻灯片

图 6 金州湾2017年围填海专题图

Figure 6. Reclamation change thematic map of Jinzhou Bay in 2017

下载: 全尺寸图片幻灯片

图 7 浮筏养殖结构图

Figure 7. Remote sensing image of floating raft

下载: 全尺寸图片幻灯片

图 8 浮筏养殖遥感影像

Figure 8. Remote sensing image of floating raft

下载: 全尺寸图片幻灯片

图 9 浮筏养殖的极化散射机制示意图(①海水表面散射，②浮球表面散射，③海水浮球二面角散射，④海水浮球螺旋体散射，⑤水下表面散射，⑥水下体散射)

Figure 9. Schematic diagram of floating raft aquaculture scattering mechanism (① Scattering method of sea surface, ② Scattering method of floating raft, ③ Scattering method of dihedral caused by raft and sea surface, ④ Scattering method of helix structure, ⑤ Scattering method of surface under water, ⑥ Scattering method of body under water)

下载: 全尺寸图片幻灯片

图 10 日本明海区域海水养殖极化分解结果

Figure 10. Polar decomposition of floating raft aquaculture in Japan

下载: 全尺寸图片幻灯片

图 11 大连市浮筏养殖Yamaguchi极化分解结果

Figure 11. Yamaguchi polar decomposition of floating raft aquaculture in Dalian

下载: 全尺寸图片幻灯片

图 12 GF-3遥感影像数据切片情况

Figure 12. Imagery of GF-3 slices

下载: 全尺寸图片幻灯片

图 13 GF-3数据切片1结果图

Figure 13. Result based on GF-3 data slice 1

下载: 全尺寸图片幻灯片

图 14 GF-3数据切片2结果图

Figure 14. Result based on GF-3 data slice 2

下载: 全尺寸图片幻灯片

图 15 数据切片3处理结果

Figure 15. Result of data slice 3 experiment

下载: 全尺寸图片幻灯片

图 16 数据切片4处理结果

Figure 16. Result of data slice 4 experiment

下载: 全尺寸图片幻灯片

图 17 数据切片5处理结果

Figure 17. Result of data slice 5 experiment

下载: 全尺寸图片幻灯片

图 18 极化浮筏养殖识别结果(全极化模式Ⅰ)

Figure 18. Floating raft recognition result under full polarmetric mode Ⅰ

下载: 全尺寸图片幻灯片

图 19 UFS浮筏养殖识别结果(超精细条带模式)

Figure 19. Identification result of UFS floating raft

下载: 全尺寸图片幻灯片

表 1 GF-3卫星SAR数据属性信息

Table 1. Attribute information of SAR data on GF-3

序号	成像模式		分辨率(m)			成像幅宽(km)		入射角范围(°)	极化方式
序号	成像模式		标称	方位向	距离向	标称	范围	入射角范围(°)	极化方式
1	聚束		1	1.0～1.5	0.9～2.5	10×10	10×10	20～50	可选单极化
2	超精细条带		3	3	2.5～5.0	30	30	20～50	可选单极化
3	精细条带Ⅰ		5	5	4～6	50	50	19～50	可选双极化
4	精细条带Ⅱ		10	10	8～12	100	95～110	19～50	可选双极化
5	标准条带		25	25	15～30	130	95～150	17～50	可选双极化
6	窄幅扫描Ⅰ		50	50～60	30～60	300	300	17～50	可选双极化
7	窄幅扫描Ⅱ		100	100	50～110	500	500	17～50	可选双极化
8	全极化条带Ⅰ		8	8	6～9	30	20～35	20～41	全极化
9	全极化条带Ⅱ		25	25	15～30	40	35～50	20～38	全极化
10	波成像模式		10	10	8～12	5×5	5×5	20～41	全极化
11	全球观测成像模式		500	500	350～700	650	650	17～53	可选双极化
12	扩展入射角	低入射角	25	25	15～30	130	120～150	10～20	可选双极化
12	扩展入射角	高入射角	25	25	20～30	80	70～90	50～60	可选双极化

下载: 导出CSV

表 2 精度评价结果

Table 2. The results of precision assessment

区域	RMSE波动范围(像素数)	位置相同像素点	位置不同像素点	准确率
大连金州湾	0.5～1.5	46	4	92%

下载: 导出CSV

表 3 精度评价结果

Table 3. The results of precision assessment

区域	单极化聚类结果(%)	Yamaguchi极化特征聚类结果(%)	H/A/Alpha极化特征聚类结果(%)
区域1	71.33	78.69	62.28
区域2	71.54	79.17	61.31

下载: 导出CSV

参考文献(58)

[1]	潘德炉, 林明森, 毛志华. 海洋微波遥感与应用[M]. 北京: 海洋出版社, 2014. Pan De-lu, Lin Ming-sen, and Mao Zhi-hua. Application on Ocean Microwave Remote Sensing[M]. Beijing: China Ocean Press, 2014.
[2]	何宜军, 孟雷, 李海艳, 等. 全极化合成孔径雷达海浪遥感方法[J]. 遥感技术与应用, 2007, 22(2): 177–182. doi: 10.11873/j.issn.1004-0323.2007.2.177 He Yi-jun, Meng Lei, Li Hai-yan, et al.. Ocean wave measured by fully polarimetric synthetic aperture radar[J]. Remote Sensing Technology and Application, 2007, 22(2): 177–182. doi: 10.11873/j.issn.1004-0323.2007.2.177
[3]	韩富伟, 苗丰民, 赵建华, 等. 3S技术在海域使用动态监测中的应用[J]. 海洋环境科学, 2008, 27(S2): 85–89. http://www.cnki.com.cn/Article/CJFDTOTAL-HYHJ2008S2019.htm Han Fu-wei, Miao Feng-min, Zhao Jian-hua, et al.. Application of 3S technique in dynamic monitoring in sea usage[J]. Marine Environmental Science, 2008, 27(S2): 85–89. http://www.cnki.com.cn/Article/CJFDTOTAL-HYHJ2008S2019.htm
[4]	王厚军, 丁宁, 赵建华, 等. 海域动态监视监测业务分析研究[J]. 海洋开发与管理, 2017, 34(1): 39–41. http://youxian.cnki.com.cn/yxdetail.aspx?filename=LDAX20171026000&dbname=CAPJ2015 Wang Hou-jun, Ding Ning, Zhao Jian-hua, et al.. Operational analysis of dynamic surveillance monitoring of sea area[J]. Ocean Development and Management, 2017, 34(1): 39–41. http://youxian.cnki.com.cn/yxdetail.aspx?filename=LDAX20171026000&dbname=CAPJ2015
[5]	吴良斌. SAR图像处理与目标识别[M]. 北京: 航空工业出版社, 2013. Wu Liang-bin. SAR Image Processing and Target Recognition[M]. Beijing: Aviation Industry Press, 2013.
[6]	丁赤飚, 刘佳音, 雷斌, 等. 高分三号SAR卫星系统级几何定位精度初探[J]. 雷达学报, 2017, 6(1): 11–16, DOI: 10.12000/JR17024. Ding Chi-biao, Liu Jia-yin, Lei Bin, et al.. Preliminary exploration of systematic geolocation accuracy of GF-3 SAR satellite system[J]. Journal of Radars, 2017, 6(1): 11–16, DOI: 10.12000/JR17024.
[7]	杨虎, 郭华东, 王长林. TM-SAR数据融合在黄河口沙咀动态监测中的应用研究[J]. 地理学与国土研究, 2001, 17(4): 15–19. http://www.cnki.com.cn/Article/CJFDTOTAL-DLGT200104003.htm Yang Hu, Guo Hua-dong, and Wang Chang-lin. Coast line dynamic inspect and land cover classification at Yellow River Mouth using TM-SAR data fusion method[J]. Geography and Territorial Research, 2001, 17(4): 15–19. http://www.cnki.com.cn/Article/CJFDTOTAL-DLGT200104003.htm
[8]	陆立明, 王润生, 李武皋. 基于合成孔径雷达回波数据的海岸线提取方法[J]. 软件学报, 2004, 15(4): 531–536. http://www.cnki.com.cn/Article/CJFDTOTAL-RJXB200404008.htm Lu Li-ming, Wang Run-sheng, and Li Wu-gao. A method of coastline extraction from synthetic aperture radar raw-data[J]. Journal of Software, 2004, 15(4): 531–536. http://www.cnki.com.cn/Article/CJFDTOTAL-RJXB200404008.htm
[9]	林维诗. 基于主动轮廓模型和水平集方法的图像分割[D]. [硕士论文], 西安电子科技大学, 2010. Lin Wei-shi. Image segmentation based on active contour model and level set method[D]. [Master dissertation], Xidian University, 2010.
[10]	赵伟舟. 基于模糊理论的SAR图像分割算法研究与实现[J]. 电脑知识与技术, 2008, 3(4): 768–769, 779. Zhao Wei-zhou. Research and realization of an algorithm in SAR image segmentation based on fuzzy theory[J]. Computer Knowledge and Technology, 2008, 3(4): 768–769, 779.
[11]	李映, 史勤峰, 张艳宁, 等. SAR图像的自动分割方法研究[J]. 电子与信息学报, 2006, 28(5): 932–935. http://youxian.cnki.com.cn/yxdetail.aspx?filename=LDAX20171026000&dbname=CAPJ2015 Li Ying, Shi Qin-feng, Zhang Yan-ning, et al.. Automatic segmentation for synthetic aperture radar images[J]. Journal of Electronics & Information Technology, 2006, 28(5): 932–935. http://youxian.cnki.com.cn/yxdetail.aspx?filename=LDAX20171026000&dbname=CAPJ2015
[12]	荆浩, 陈学佺, 顾志伟. 一种基于边缘特征的海岸线检测方法[J]. 计算机仿真, 2006, 23(8): 89–93, 101. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJZ200608025.htm Jing Hao, Chen Xue-quan, and Gu Zhi-wei. A method for coastline extraction based on edges[J]. Computer Simulation, 2006, 23(8): 89–93, 101. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJZ200608025.htm
[13]	Kass M, Witkin A, and Terzopoulos D. Snakes: Active contour models[J]. International Journal of Computer Vision, 1988, 1(4): 321–331. doi: 10.1007/BF00133570
[14]	Moctezuma M, Escalante B, Mendez R, et al.. Coastline detection with polynomial transforms and Markovian segmentations[C]. Proceedings of the 1997 IEEE International Geoscience and Remote Sensing, Remote Sensing-A Scientific Vision for Sustainable Development, Singapore, 1997, 1: 38–40.
[15]	Sethian J A. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science[M]. Cambridge: Cambridge University Press, 2000.
[16]	张秀英, 钟太洋, 黄贤金, 等. 海州湾生态系统服务价值评估[J]. 生态学报, 2013, 33(2): 640–649. http://www.cnki.com.cn/Article/CJFDTOTAL-STXB201302037.htm Zhang Xiu-ying, Zhong Tai-yang, Huang Xian-jin, et al.. Values of marine ecosystem services in Haizhou Bay[J]. Acta Ecologica Sinica, 2013, 33(2): 640–649. http://www.cnki.com.cn/Article/CJFDTOTAL-STXB201302037.htm
[17]	范剑超, 王涛. 基于非负矩阵分解和支持向量机的墨西哥湾溢油HJ-1星遥感图像分类[J]. 海洋环境科学, 2015, 34(3): 441–446. http://www.cnki.com.cn/Article/CJFDTOTAL-HYHJ201503021.htm Fan Jian-chao and Wang Tao. HJ-1 satellite remote sensing image segmentation in the oil spill of Mexico Gulf base on the non-negative matrix factorization and support vector machine[J]. Marine Environmental Science, 2015, 34(3): 441–446. http://www.cnki.com.cn/Article/CJFDTOTAL-HYHJ201503021.htm
[18]	范剑超, 张丰收, 赵冬至, 等. 基于高分辨率卫星遥感SAR图像的海洋浮筏养殖信息提取[C]. 第二届中国沿海地区灾害风险分析与管理学术研讨会论文集, 海口, 中国, 2014: 59–63. Fan Jian-chao, Zhang Feng-shou, Zhao Dong-zhi, et al.. Floating raft aquaculture extraction based on high resolution satellite remote sensing SAR images[C]. Second Symposium on Disaster Risk Analysis and Management in Chinese Littoral Regions, Haikou, China, 2014: 59–63.
[19]	樊建勇, 黄海军, 樊辉, 等. 利用RADARSAT-1数据提取海水养殖区面积[J]. 海洋科学, 2005, 29(10): 44–47. doi: 10.3969/j.issn.1000-3096.2005.10.011 Fan Jian-yong, Huang Hai-jun, Fan Hui, et al.. Extracting aquaculture area with RADASAT-1[J]. Marine Sciences, 2005, 29(10): 44–47. doi: 10.3969/j.issn.1000-3096.2005.10.011
[20]	卢业伟, 李强子, 杜鑫, 等. 基于高分辨率影像的近海养殖区的一种自动提取方法[J]. 遥感技术与应用, 2015, 30(3): 486–494. http://www.cnki.com.cn/Article/CJFDTOTAL-YGJS201503012.htm Lu Ye-wei, Li Qiang-zi, Du Xin, et al.. A method of coastal aquaculture area automatic extraction with high spatial resolution images[J]. Remote Sensing Technology and Application, 2015, 30(3): 486–494. http://www.cnki.com.cn/Article/CJFDTOTAL-YGJS201503012.htm
[21]	Van der Wal D, Herman P M J, and Wielemaker-van den Dool A. Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery[J]. Remote Sensing of Environment, 2005, 98(1): 96–109. doi: 10.1016/j.rse.2005.06.004
[22]	Lee S K, Hong S H, Kim S W, et al.. Polarimetric features of oyster farm observed by AIRSAR and JERS-1[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2728–2735. doi: 10.1109/TGRS.2006.879107
[23]	Choe B H, Kim D J, Hwang J H, et al.. Detection of oyster habitat in tidal flats using multi-frequency polarimetric SAR data[J]. Estuarine, Coastal and Shelf Science, 2012, 97: 28–37. doi: 10.1016/j.ecss.2011.11.007
[24]	Gade M and Melchionna S. Joint use of multiple Synthetic Aperture Radar imagery for the detection of bivalve beds and morphological changes on intertidal flats[J]. Estuarine, Coastal and Shelf Science, 2016, 171: 1–10. doi: 10.1016/j.ecss.2016.01.025
[25]	初佳兰, 赵冬至, 张丰收, 等. 基于卫星遥感的浮筏养殖监测技术初探—以长海县为例[J]. 海洋环境科学, 2008, 27(S2): 35–40. http://www.cnki.com.cn/Article/CJFDTOTAL-HYHJ2008S2007.htm Chu Jia-lan, Zhao Dong-zhi, Zhang Feng-shou, et al.. Monitor method of rafts cultivation by remote sense—A case of Changhai[J]. Marine Environmental Science, 2008, 27(S2): 35–40. http://www.cnki.com.cn/Article/CJFDTOTAL-HYHJ2008S2007.htm
[26]	Fan Jian-chao, Chu Jia-lan, Geng Jie, et al.. Floating raft aquaculture information automatic extraction based on high resolution SAR images[C]. 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italian, 2015: 3898–3901.
[27]	Mostafa A A, Debes C, and Zoubir A M. Segmentation by classification for through-the-wall radar imaging using polarization signatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(9): 3425–3439. doi: 10.1109/TGRS.2011.2181951
[28]	Hu Wei-ming, Hu Wei, Xie Nian-hua, et al.. Unsupervised active learning based on hierarchical graph-theoretic clustering[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39(5): 1147–1161. doi: 10.1109/TSMCB.2009.2013197
[29]	Gou Shui-ping, Zhuang Xiong, Zhu Hu-ming, et al.. Parallel sparse spectral clustering for SAR image segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(4): 1949–1963. doi: 10.1109/JSTARS.2012.2230435
[30]	Kiranyaz S, Ince T, Uhlmann S, et al.. Collective network of binary classifier framework for polarimetric SAR image classification: An evolutionary approach[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(4): 1169–1186. doi: 10.1109/TSMCB.2012.2187891
[31]	邹鹏飞, 李震, 田帮森. 高分辨率极化SAR图像水平集分割[J]. 中国图象图形学报, 2014, 19(12): 1829–1835. doi: 10.11834/jig.20141215 Zou Peng-fei, Li Zhen, and Tian Bang-sen. High-resolution PolSAR image level set segmentation[J]. Journal of Image and Graphics, 2014, 19(12): 1829–1835. doi: 10.11834/jig.20141215
[32]	Wu Yong-hui, Ji Ke-feng, Yu Wen-xian, et al.. Region-based classification of polarimetric SAR images using Wishart MRF[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 668–672. doi: 10.1109/LGRS.2008.2002263
[33]	Yu P, Qin A K, and Clausi D A. Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1302–1317. doi: 10.1109/TGRS.2011.2164085
[34]	Fan Jian-chao, Han Min, and Wang Jun. Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation[J]. Pattern Recognition, 2009, 42(11): 2527–2540. doi: 10.1016/j.patcog.2009.04.013
[35]	Fan Jian-chao and Wang Jun. A two-pass unsupervised clustering algorithm for polarimetric SAR image segmentation[C]. IEEE OCEANS’13/MTS, Bergen, Norway, 2013: 1–5.
[36]	Maitre H. Processing of Synthetic Aperture Radar Images[M]. London, UK: Wiley, 2008.
[37]	Clausi D A and Deng Huang. Design-based texture feature fusion using Gabor filters and co-occurrence probabilities[J]. IEEE Transactions on Image Processing, 2005, 14(7): 925–936. doi: 10.1109/TIP.2005.849319
[38]	Xiang De-liang, Tang Tao, Hu Can-bin, et al.. A kernel clustering algorithm with fuzzy factor: Application to SAR image segmentation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(7): 1290–1294. doi: 10.1109/LGRS.2013.2292820
[39]	Cunha A L, Zhou Jian-ping, and Do M N. The nonsubsampled contourlet transform: Theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089–3101. doi: 10.1109/TIP.2006.877507
[40]	余航, 焦李成, 刘芳. 基于上下文分析的无监督分层迭代算法用于SAR图像分割[J]. 自动化学报, 2014, 40(1): 100–116. http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201401012.htm Yu Hang, Jiao Li-cheng, and Liu Fang. Context based unsupervised hierarchical iterative algorithm for SAR segmentation[J]. Acta Automatica Sinica, 2014, 40(1): 100–116. http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201401012.htm
[41]	林达, 徐新, 董浩, 等. 一种有效的MSTAR SAR图像分割方法[J]. 武汉大学学报(信息科学版), 2015, 40(10): 1377–1380, 1385. doi: 10.13203/j.whugis20130572 Lin Da, Xu Xin, Dong Hao, et al.. An effective segmentation algorithm for MSTAR SAR target chips[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1377–1380, 1385. doi: 10.13203/j.whugis20130572
[42]	Chen Long, Chen C L P, and Lu Ming-zhu. A multiple-kernel fuzzy C-means algorithm for image segmentation[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 41(5): 1263–1274. doi: 10.1109/TSMCB.2011.2124455
[43]	Huang H C, Chuang Y Y, and Chen Chu-song. Multiple kernel fuzzy clustering[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(1): 120–134. doi: 10.1109/TFUZZ.2011.2170175
[44]	LeCun Y, Bengio Y, and Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
[45]	Farabet C, Couprie C, Najman L, et al.. Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915–1929. doi: 10.1109/TPAMI.2012.231
[46]	Huang Yan, Wang Wei, Wang Liang, et al.. A general nonlinear embedding framework based on deep neural network[C]. 22nd International Conference on Pattern Recognition, Stockholm, 2014: 732–737.
[47]	Zhang Liang-pei, Zhang Le-fei, and Du Bo. Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2): 22–40. doi: 10.1109/MGRS.2016.2540798
[48]	邓磊, 付姗姗, 张儒侠. 深度置信网络在极化SAR图像分类中的应用[J]. 中国图象图形学报, 2016, 21(7): 933–941. doi: 10.11834/jig.20160711 Deng Lei, Fu Shan-shan, and Zhang Ru-xia. Application of deep belief network in polarimetric SAR image classification[J]. Journal of Image and Graphics, 2016, 21(7): 933–941. doi: 10.11834/jig.20160711
[49]	Chen Si-zhe, Wang Hai-peng, Xu Feng, 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
[50]	王云艳, 何楚, 赵守能, 等. 基于多层反卷积网络的SAR图像分类[J]. 武汉大学学报·信息科学版, 2015, 40(10): 1371–1376. doi: 10.13203/j.whugis20140366 Wang Yun-yan, He Chu, Zhao Shou-neng, et al.. Classification of SAR images based on deep deconvolutional network[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1371–1376. doi: 10.13203/j.whugis20140366
[51]	丁军, 刘宏伟, 陈渤, 等. 相似性约束的深度置信网络在SAR图像目标识别的应用[J]. 电子与信息学报, 2016, 38(1): 97–103. doi: 10.11999/JEIT150366 Ding Jun, Liu Hong-wei, Chen Bo, et al.. Similarity constrained deep belief networks with application to SAR image target recognition[J]. Journal of Electronics & Information Technology, 2016, 38(1): 97–103. doi: 10.11999/JEIT150366
[52]	Jiao Li-cheng and Liu Fang. Wishart deep stacking network for fast POLSAR image classification[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3273–3286. doi: 10.1109/TIP.2016.2567069
[53]	Liu Jia, Gong Mao-guo, Zhao Jiao-jiao, et al.. Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images[J]. Soft Computing, 2016, 20(12): 4645–4657. doi: 10.1007/s00500-014-1460-0
[54]	Qin Fa-chao, Guo Ji-ming, and Sun Wei-dong. Object-oriented ensemble classification for polarimetric SAR Imagery using restricted Boltzmann machines[J]. Remote Sensing Letters, 2017, 8(3): 204–213. doi: 10.1080/2150704X.2016.1258128
[55]	Zhao Zhi-qiang, Jiao Li-cheng, Zhao Jia-qi, et al.. Discriminant deep belief network for high-resolution SAR image classification[J]. Pattern Recognition, 2017, 61: 686–701. doi: 10.1016/j.patcog.2016.05.028
[56]	Geng Jie, Fan Jian-chao, Wang Hong-yu, et al.. High-resolution SAR image classification via deep convolutional autoencoders[J]. IEEE Geoscience and Remote Sensing Letter, 2015, 12(11): 2351–2355. doi: 10.1109/LGRS.2015.2478256
[57]	Geng Jie, Wang Hong-yu, Fan Jian-chao, et al.. Deep supervised and contractive neural network for SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4): 2442–2459. doi: 10.1109/TGRS.2016.2645226
[58]	耿杰, 范剑超, 初佳兰, 等. 基于深度协同稀疏编码网络的海洋浮筏SAR图像目标识别[J]. 自动化学报, 2016, 42(4): 593–604. http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201604010.htm Geng Jie, Fan Jian-chao, Chu Jia-lan, et al.. Research on marine floating raft aquaculture SAR image target recognition based on deep collaborative sparse coding network[J]. Acta Automatica Sinica, 2016, 42(4): 593–604. http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201604010.htm