极化特征组合在ALOS PALSAR数据地物分类中的应用

doi:10.13474/j.cnki.11-2246.2018.0204

测绘通报 ›› 2018, Vol. 0 ›› Issue (7): 29-33,47.doi: 10.13474/j.cnki.11-2246.2018.0204

极化特征组合在ALOS PALSAR数据地物分类中的应用

贾诗超¹, 薛东剑^1,2, 李成绕¹

1. 成都理工大学地球科学学院, 四川成都 610059;
2. 国土资源部地学空间信息技术重点实验室, 四川成都 610059

收稿日期:2017-09-21 出版日期:2018-07-25 发布日期:2018-08-02
通讯作者: 薛东剑。E-mail:xdj101@sina.com E-mail:xdj101@sina.com
作者简介:贾诗超(1993-),男,硕士生,主要从事SAR图像分类研究。E-mail:923137407@qq.com
基金资助:
四川省教育厅重点项目（16ZA0100）；国土资源部地学空间信息技术重点实验室开放基金（KLGSIT2013-06）

Application of Polarization Characteristic Combination in ALOS PALSAR Data Classification

JIA Shichao¹, XUE Dongjian^1,2, LI Chengrao¹

1. College of Earth Sciences of Chengdu University, Chengdu 610059, China;
2. Key Laboratory of Geoscience Spatial Information Technology of Ministry of Land and Resources, Chengdu 610059, China

Received:2017-09-21 Online:2018-07-25 Published:2018-08-02

摘要/Abstract

摘要： 极化SAR图像分类是目前遥感领域研究热点之一，它为地物信息获取和地物分类提供了新方法。文中对四川省彭州石化地区利用ALOS PALSAR全极化数据进行支持向量机（SVM）分类。试验中首先获得极化总功率，然后对数据进行Cloude-Pottier极化分解，再基于相干矩阵的特征值，提取特征参数香农熵和雷达植被指数。组合这些极化特征对影像进行SVM的分类，并与基于Freeman-Durden极化分解的SVM分类和Wishart监督分类进行比较。试验结果表明：本文采用的极化特征组合信息之间得到相互补充，分类结果效果较好，Kappa系数为97.14%，相对另两种方法的Kappa系数分别提高了5.26%和27.20%。

关键词: ALOS PALSAR, 极化特征组合, 支持向量机, Cloude-Pottier分解, 地物分类

Abstract: Polarimetric SAR image classification is one of the hots pots in the field of remote sensing,which provides a new means for information acquisition and classification of objects.In this paper,support vector machine (SVM) is used to classify ALOS PALSAR polarized data in Pengzhou petrochemical area,Sichuan province.In the experiment,the total power of polarization is firstly obtained,then the Cloude-Pottier polarization is decomposed,and then the feature parameters,Shannon entropy and radar vegetation index are extracted based on the eigenvalues of the coherent matrix.The SVM classification of the images is carried out by combining these polarimetric features,and compared with the SVM classification based on Freeman-Durden polarization decomposition and Wishart supervised classification.The experimental results show that the polarization characteristic combined with the information is complementary and the classification result is better,the Kappa coefficient is 97.14%,and the Kappa coefficient of the other two methods increased by 5.26% and 27.20%,respectively.

Key words: ALOS PALSAR, polarization characteristic combination, support vector machine, Cloude-Pottier decomposition, classification of objects

中图分类号:

P237

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.

JIA Shichao, XUE Dongjian, LI Chengrao. Application of Polarization Characteristic Combination in ALOS PALSAR Data Classification[J]. 测绘通报, 2018, 0(7): 29-33,47.

参考文献

[1] 张红,王超.极化SAR理论、方法与应用[M].北京:科学出版社,2015.
[2] HUYNEN J R.Phenomenological Theory of Radar Targets[J].Electromagnetic Scattering,1978:653-712.
[3] WANG S,LIU K,PEI J,et al.Unsupervised Classification of Fully Polarimetric SAR Images Based on Scattering Power Entropy and Copolarized Ratio[J].IEEE Geoscience and Remote Sensing Letters,2013,10(3):622-626.
[4] XUE X,DI L,GUO L,et al.An Efficient Classification Method of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features[C]//International Conference on Agro-Geoinformatics.Istanbul:IEEE,2015.
[5] VAN ZYL J J,BURNETTE C F.Bayesian Classification of Polarimetric SAR Images Using Adaptive a Priori Probabilities[J].International Journal of Remote Sensing,1992,13(5):835-840.
[6] 周晓光,匡纲要,万建伟.极化SAR图像分类综述[J].信号处理,2008,24(5):806-812.
[7] FUKUDA S,HIROSAWA H.Support Vector Machine Classification of Land Cover:Application to Polarimetric SAR Data[C]//Proceedings of IGARSS '01.Sydney,Australia:[s.n.],2001:187-189.
[8] 汪洋,鲁加国,张长耀.基于Cameron分解和SVM的极化SAR图像分类[J].计算机工程与应用,2006,42(36):17-19.
[9] 裴静静.基于Freeman分解的极化SAR图像分类研究[D].西安:西安电子科技大学,2012.
[10] 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,2009,42(36):5-7.
[11] 马騳骉,卢春燕.基于联合特征和SVM的极化SAR图像分类方法[J].无线电工程,2017,47(3):31-34.
[12] 曲永超,赵书河,庄喜阳,等.基于特征值和Singh分解的全极化Radarsat-2图像分类[J].地理空间信息,2016,14(5):60-63.
[13] CLOUDE S R,POTTIER E.An Entropy Based Classification Scheme for Land Applications of Polarimetric SAR[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(1):68-78.
[14] MORIO J,REFREGIER P,GOUDAIL F,et al.Application of Information Theory Measures to Polarimetric and Interferometric SAR Images[C]//PSIP.Mulhouse,France:[s.n.],2007.
[15] MORIO J,REFREGIER P.Shannon Entropy of Partially Polarized and Partially Coherent Light with Gaussian Fluctuations[J].Journal of the Optical Society of America A,2006,23(12):3036-3044.
[16] ZYL J J V.Application of Cloude's Target Decomposition Theorem to Polarimetric Imaging Radar Data[C]//Proceedings of SPIE. San Diego:[s.n.],1993:184-191.
[17] 陈媛媛,何秀凤,王静.基于SVM的极化SAR沿海滩涂分类[J].地理空间信息,2015,13(6):150-153.
[18] 冯青.基因微阵列数据的SVM分类器优化方法[D].长春:东北师范大学,2010.
[19] LEE J S,GRUNES M R.Classification of Multi-look Polarimetric SAR Data Based on Complex Wishart Distribution[J].International Journal of Remote Sensing,1994,15(11):2299-2311.
[20] 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.
[21] 赵一博,秦先祥,邹焕新.基于目标分解和SVM的极化SAR图像分类方法[J].航天返回与遥感,2013,34(2):50-56.

极化特征组合在ALOS PALSAR数据地物分类中的应用

Application of Polarization Characteristic Combination in ALOS PALSAR Data Classification

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王晓晓, 韩留生, 杨骥, 李勇, 张大富, 孙广伟, 范俊甫. Sentinel-2与Landsat 8数据组合下的多特征冬小麦面积提取[J]. 测绘通报, 2022, 0(3): 111-115.
[2]	郭云开, 曹骁, 谢晓峰, 张思爱, 谢琼. 基于集合经验模态分解的土壤重金属Cd定量反演[J]. 测绘通报, 2022, 0(1): 44-49.
[3]	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.
[4]	张鹏, 马庆勋, 吕杰, 季金亮, 李紫微. 机器学习算法在森林地上生物量估算中的应用[J]. 测绘通报, 2021, 0(12): 28-32.
[5]	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.
[6]	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.
[7]	孔晖, 金洪芳. 一种基于联合特征的地表覆盖变化自动检测方法[J]. 测绘通报, 2020, 0(10): 110-113,122.
[8]	王慧, 冯启言, 刁鑫鹏, 孟磊. 煤炭开采对泥河小流域土地利用及景观格局的影响[J]. 测绘通报, 2019, 0(9): 27-33,61.
[9]	张启华, 王胜利, 孙磊, 蒋毅. 空-谱角匹配与多指数法相结合的OLI遥感影像水体提取[J]. 测绘通报, 2019, 0(12): 71-76.
[10]	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.
[11]	程效猛, 郑浩, 眭海刚, 冯文卿. 结合矢量引导的高分辨率遥感影像道路自动提取[J]. 测绘通报, 2018, 0(9): 19-23.
[12]	张伟, 吴艳民, 刘吉凯. Landsat系列影像支持下的凤阳县石英砂矿区土地利用动态监测[J]. 测绘通报, 2018, 0(6): 91-97.
[13]	刘小生, 于良. 改进的SVR在大坝边坡位移预测中的应用[J]. 测绘通报, 2018, 0(6): 122-125.
[14]	郭际明, 王威, 张帅. 支持向量机辅助下的智能手机行人运动模式识别[J]. 测绘通报, 2018, 0(2): 1-5.
[15]	王胜利, 张连蓬, 朱寿红, 吉莉, 柴琪, 沈扬, 张蕊. 多共性特征联合的Landsat 8 OLI遥感影像光伏电站提取[J]. 测绘通报, 2018, 0(11): 46-52.