引入极化方位角特征的极化SAR图像分类
针对传统的基于散射机理的极化SAR图像分类方法易导致与雷达方位向具有一定倾角的建筑物（简称定向建筑物）被错划为森林等体散射类型的问题,提出一种引入极化方位角特征的分类方法。利用四分量分解模型并引入极化方位角补偿技术把像元划分为相应的主散射类型;定义一种极化方位角标准差参数作为区域匀质性测量指标,利用该参数从体散射类型中区分出定向建筑物类型;并在此基础上将Wishart分类器应用于极化SAR图像分类。采用E-SAR系统获取的L波段全极化数据进行实验,并与传统分类方法进行对比。定性和定量的比较结果表明,提出的方法不仅保留了传统分类方法的优势,且很好地解决了定向建筑物与森林的分类混淆现象。...
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| Published in | 计算机应用研究 Vol. 32; no. 11; pp. 3484 - 3488 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国科学院遥感与数字地球研究所 数字地球重点实验室，北京 100094
2015
中国科学院大学，北京 100049%中国科学院遥感与数字地球研究所 数字地球重点实验室,北京,100094 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2015.11.067 |
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| Abstract | 针对传统的基于散射机理的极化SAR图像分类方法易导致与雷达方位向具有一定倾角的建筑物（简称定向建筑物）被错划为森林等体散射类型的问题,提出一种引入极化方位角特征的分类方法。利用四分量分解模型并引入极化方位角补偿技术把像元划分为相应的主散射类型;定义一种极化方位角标准差参数作为区域匀质性测量指标,利用该参数从体散射类型中区分出定向建筑物类型;并在此基础上将Wishart分类器应用于极化SAR图像分类。采用E-SAR系统获取的L波段全极化数据进行实验,并与传统分类方法进行对比。定性和定量的比较结果表明,提出的方法不仅保留了传统分类方法的优势,且很好地解决了定向建筑物与森林的分类混淆现象。 |
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| AbstractList | TP391.41; 针对传统的基于散射机理的极化 SAR 图像分类方法易导致与雷达方位向具有一定倾角的建筑物（简称定向建筑物）被错划为森林等体散射类型的问题，提出一种引入极化方位角特征的分类方法。利用四分量分解模型并引入极化方位角补偿技术把像元划分为相应的主散射类型；定义一种极化方位角标准差参数作为区域匀质性测量指标，利用该参数从体散射类型中区分出定向建筑物类型；并在此基础上将 Wishart 分类器应用于极化 SAR 图像分类。采用 E-SAR 系统获取的 L 波段全极化数据进行实验，并与传统分类方法进行对比。定性和定量的比较结果表明，提出的方法不仅保留了传统分类方法的优势，且很好地解决了定向建筑物与森林的分类混淆现象。 针对传统的基于散射机理的极化SAR图像分类方法易导致与雷达方位向具有一定倾角的建筑物（简称定向建筑物）被错划为森林等体散射类型的问题,提出一种引入极化方位角特征的分类方法。利用四分量分解模型并引入极化方位角补偿技术把像元划分为相应的主散射类型;定义一种极化方位角标准差参数作为区域匀质性测量指标,利用该参数从体散射类型中区分出定向建筑物类型;并在此基础上将Wishart分类器应用于极化SAR图像分类。采用E-SAR系统获取的L波段全极化数据进行实验,并与传统分类方法进行对比。定性和定量的比较结果表明,提出的方法不仅保留了传统分类方法的优势,且很好地解决了定向建筑物与森林的分类混淆现象。 |
| Abstract_FL | The conventional PolSAR image classification algorithm based on the scattering mechanism tends to misclassify the oriented buildings (not parallel to the radar azimuth)into the volume scattering category such as the forests.To overcome this problem,this paper developed an improved classification method by introducing polarization orientation angle (POA)feature. Firstly,it divided all pixels into corresponding dominated scattering categories by applying four-component decomposition mo-del combined with the polarization orientation angle compensation.Then,it defined a parameter,POA standard deviation,as an indicator of the region homogeneity measure and used this parameter to distinguish the oriented buildings from the volume scattering category.Based on this,it applied the Wishart iterative classifier to the PolSAR image classification.Using the E-SAR L band full polarimetric SAR data,this paper made a comparison between the proposed method and the conventional classification algorithm.The quantitative and qualitative comparisons result proves that the proposed method not only preserves the advantage of the conventional classification,but also effectively eliminates the classification confusion between the oriented buildings and the forests. |
| Author | 王剑波 王超 张红 吴樊 |
| AuthorAffiliation | 中国科学院遥感与数字地球研究所数字地球重点实验室,北京100094 中国科学院大学,北京100049 |
| AuthorAffiliation_xml | – name: 中国科学院遥感与数字地球研究所 数字地球重点实验室，北京 100094; 中国科学院大学，北京 100049%中国科学院遥感与数字地球研究所 数字地球重点实验室,北京,100094 |
| Author_FL | Zhang Hong Wu Fan Wang Jianbo Wang Chao |
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| ClassificationCodes | TP391.41 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | Polarimetric SAR image classification by introducing polarization orientation angle feature |
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| Keywords | 极化方位角 four-component decomposition 图像分类 Wishart 迭代 image classification polariza-tion orientation angle (POA) Wishart iteration 极化合成孔径雷达 四分量分解 polarimetric synthetic aperture radar (PolSAR) |
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| Notes | 51-1196/TP The conventional PolSAR image classification algorithm based on the scattering mechanism tends to misclassify the oriented buildings （not parallel to the radar azimuth） into the volume scattering category such as the forests. To overcome this problem, this paper developed an improved classification method by introducing polarization orientation angle （POA） teature. Firstly, it divided all pixels into corresponding dominated scattering categories by applying four-component decomposition mo- del combined with the polarization orientation angle compensation. Then, it defined a parameter, POA standard deviation, as an indicator of the region homogeneity measure and used this parameter to distinguish the oriented buildings from the volume scattering category. Based on this, it applied the Wishart iterative classifier to the PolSAR image classification. Using the E-SAR L band full polarimetric SAR data, this paper made a comparison between the proposed method and the conventional classification algorithm. |
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| PublicationTitle | 计算机应用研究 |
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| Publisher | 中国科学院遥感与数字地球研究所 数字地球重点实验室，北京 100094 中国科学院大学，北京 100049%中国科学院遥感与数字地球研究所 数字地球重点实验室,北京,100094 |
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| Snippet | 针对传统的基于散射机理的极化SAR图像分类方法易导致与雷达方位向具有一定倾角的建筑物（简称定向建筑物）被错划为森林等体散射类型的问题,提出一种引入极化方位角特征的... TP391.41; 针对传统的基于散射机理的极化 SAR 图像分类方法易导致与雷达方位向具有一定倾角的建筑物（简称定向建筑物）被错划为森林等体散射类型的问题，提出一种引入极化... |
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| SubjectTerms | Wishart迭代 四分量分解 图像分类 极化合成孔径雷达 极化方位角 |
| Title | 引入极化方位角特征的极化SAR图像分类 |
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