结合Singh四分量分解的高分三号全极化SAR影像分类

doi:10.13474/j.cnki.11-2246.2020.0050

测绘通报 ›› 2020, Vol. 0 ›› Issue (2): 86-89.doi: 10.13474/j.cnki.11-2246.2020.0050

结合Singh四分量分解的高分三号全极化SAR影像分类

王松松^1,2, 张永红², 孔祥意^1,2

1. 山东科技大学测绘科学与工程学院, 山东青岛 266590;
2. 中国测绘科学研究院, 北京 100830

收稿日期:2019-06-10 修回日期:2019-08-06 出版日期:2020-02-25 发布日期:2020-03-04
作者简介:王松松(1993-),男,硕士生,主要从事极化SAR分类。E-mail:wss931025@126.com
基金资助:
国家自然科学基金（41801284）；中国测绘科学研究院基本科研业务费（7771624）

Classification of GF-3 fully polarimetric SAR image combined with the Singh four-component decomposition

WANG Songsong^1,2, ZHANG Yonghong², KONG Xiangyi^1,2

1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;
2. Chinese Academy of Surveying and Mapping, Beijing 100830, China

Received:2019-06-10 Revised:2019-08-06 Online:2020-02-25 Published:2020-03-04

摘要/Abstract

摘要： 不同于一般分类算法基于像素统计的分类，忽略了地物的散射特性，文中提出了一种保持地物散射特性的分类方法。这种方法将Singh提出的Singh四分量分解与基于复Wishart分布的最大似然分类器相结合，对高分三号全极化影像进行分类。利用Singh四分量分解获得表面散射、体散射、二次散射和螺旋体散射，然后将前3种基础散射分别划分为多个聚类，根据复Wishart距离进行类间合并，直到获得指定类别数，输入复Wishart分类器进行迭代分类，最后进行类别合并获得最终分类结果。试验表明本文算法具有较好的分类效果且验证了利用高分三号全极化卫星数据进行影像分类的可行性。

关键词: 极化SAR, 极化分解, 散射特性, 高分三号, 地物分类

Abstract: Different from general classification algorithm, pixel statistics based classification ignores the scattering characteristics of objects. In this paper, an classification method for preserving the scattering characteristics of objects was proposed. This method combined the Singh four-component decomposition proposed by Singh with the maximum likelihood classifier based on the complex Wishart distribution for classification of GF-3 polarimetric images. Surface scattering, volume scattering, double-bounce scattering and helix scattering were obtained by Singh four-component decomposition, then the first three basic scatterings were divided into multiple clusters respectively, and the inter-class merging was performed according to the complex Wishart distance until the specified number of categories was obtained. The Wishart classifier was used foriterative classification and merged the categories at last to obtain the final classification result. Through experiments, it was proved that the algorithm proposed in this paper had a good classification performance and verified the feasibility of using GF-3 satellite data for image classification.

Key words: PolSAR, polarization decomposition, scattering characteristics, GF-3, land-cover classification

中图分类号:

P237

王松松, 张永红, 孔祥意. 结合Singh四分量分解的高分三号全极化SAR影像分类[J]. 测绘通报, 2020, 0(2): 86-89.

WANG Songsong, ZHANG Yonghong, KONG Xiangyi. Classification of GF-3 fully polarimetric SAR image combined with the Singh four-component decomposition[J]. Bulletin of Surveying and Mapping, 2020, 0(2): 86-89.

参考文献

[1] LEE J S,POTTIER E.Polarimetric radar imaging-from basic to application[M].[S.l.]:CRC Press, 2009.
[2] 张红,王超,单子力,等.极化SAR理论、方法与应用[M].北京:科学出版社,2015:81-109.
[3] 袁新哲,林明森,刘建强,等.高分三号卫星在海洋领域的应用[J].卫星应用,2018(6):17-21.
[4] 郭睿,白雪茹,邢孟道.基于去取向理论的全极化SAR图像非监督分类[J].现代雷达,2010,32(4):35-38.
[5] 赵伶俐,杨杰,李平湘,等.极化SAR影像弱散射地物统计分类[J].遥感学报,2013,17(2):306-319.
[6] 陈斌,王宏志,徐新良,等.深度学习GoogleNet模型支持下的中分辨率遥感影像自动分类[J].测绘通报,2019(6):29-33.
[7] ZHANG Y H,ZHANG J X,ZHANG X F.Land cover classification from polarimetric SAR data based on image segmentation and decision trees[J].Canadian Journal of Remote Sensing.2015,41:40-50.
[8] 曹敏,史照良,阳建逸.蜂群智能算法的遥感影像分类方法[J].测绘学报,2013,42(5):745-751.
[9] LEE J S,GRUNES M R,AINSWORTH T L.Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing.1999,37(5):2249-2258.
[10] CLOUDE S R, POTTIER E. An entropy based classifi-cation scheme for land applications of polarimetric SAR[J].IEEE Transactions on Geoscience and Remote Sensing.1997,35(1):68-78.
[11] LEE J S,GRUNES M R, POTTIER E. Unsupervised terrain classification preserving polarimetric scattering characteri-stics[J].IEEE Transactions on Geoscience and Remote Sensing. 2004,42(4):722-731.
[12] FREEMAN A,DURDEN S L.A three-component scattering model for polarimetric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing.1998,36(3):963-973.
[13] 钟能,杨文,杨祥立,等.基于混合Wishart模型的极化SAR图像非监督分类[J].雷达学报,2017,6(5):533-540.
[14] 杨然,李坤,涂志刚,等.基于Yamaguchi分解模型的全极化SAR图像分类[J].计算机工程与应用,2009,45(36):5-7.
[15] YAMAGUCHI Y,MORIYAMA T,ISHIDO M,et al.Four-component scattering model for polarimetric SAR image decomposition[J].IEEE Transactions on Geoscience and Remote Sensing.2005,43(8):1699-1706.
[16] SINGH G,YAMAGUCHI Y,PARK S E.General four-component scattering power decomposition with unitary transformation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(5):3014-3022.

结合Singh四分量分解的高分三号全极化SAR影像分类

Classification of GF-3 fully polarimetric SAR image combined with the Singh four-component decomposition

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

本文评价

[1]	车子杰, 高飞, 吴兆福, 李振轩. 基于改进U-Net网络的多源遥感影像洪涝灾害信息提取与变化分析[J]. 测绘通报, 2022, 0(1): 26-32.
[2]	赵沛冉, 管海燕, 李迪龙, 景庄伟, 于永涛. 利用样本生成方法进行机载多光谱LiDAR数据深度学习分类[J]. 测绘通报, 2021, 0(12): 16-21.
[3]	梁雪萍, 薛东剑, 贾诗超. 超像素在多极化SAR数据分类中的应用——以ALOS PALSAR为例[J]. 测绘通报, 2020, 0(5): 107-110.
[4]	曹爽, 潘锁艳, 管海燕. 机载多光谱LiDAR的随机森林地物分类[J]. 测绘通报, 2019, 0(11): 79-84.
[5]	贾诗超, 薛东剑, 李成绕. 极化特征组合在ALOS PALSAR数据地物分类中的应用[J]. 测绘通报, 2018, 0(7): 29-33,47.
[6]	余莎莎, 余洁, 朱腾, 王彦兵. 一种改进的MCSM/H-PSO全极化SAR影像分类方法[J]. 测绘通报, 2018, 0(11): 53-57.
[7]	侯飞, 胡召玲. 地形信息辅助下的全极化SAR数据土地覆盖分类[J]. 测绘通报, 2016, 0(3): 40-43.
[8]	宋明明韩春明廖静娟. X波段和全极化P波段SAR图像配准方法研究[J]. 测绘通报, 2013, 0(4): 8-11.