基于极化分解的非高斯地形无监督分类

IF 0.6 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Zhi-Zhong Huang, Lin Zheng, Wan-Jun Yin
{"title":"基于极化分解的非高斯地形无监督分类","authors":"Zhi-Zhong Huang, Lin Zheng, Wan-Jun Yin","doi":"10.1166/jno.2023.3465","DOIUrl":null,"url":null,"abstract":"Polarimetric synthetic aperture radar (PolSAR) can provide complete polarization property of terrain. Terrain classification is the most common application of PolSAR data. In this paper, an unsupervised clustering algorithm based on Freeman-Durden decomposition (FDD) and a non-Gaussian\n K-Wishart distribution classifier is proposed. This algorithm combines an advanced statistical distribution with spatial polarization scattering information of multi-looks PolSAR data. We use the prior probability characteristics of the Markov random field model to adaptively adjust\n the cluster center to make the classification more accurate. The experiment result shows that the proposed algorithm based on non-Gaussian models can better retain the polarization information of the target and the clustering accuracy was effectively improved on the real SAR images.","PeriodicalId":16446,"journal":{"name":"Journal of Nanoelectronics and Optoelectronics","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unsupervised Classification of Non-Gaussian Terrain Based on Polarimetric Decomposition\",\"authors\":\"Zhi-Zhong Huang, Lin Zheng, Wan-Jun Yin\",\"doi\":\"10.1166/jno.2023.3465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Polarimetric synthetic aperture radar (PolSAR) can provide complete polarization property of terrain. Terrain classification is the most common application of PolSAR data. In this paper, an unsupervised clustering algorithm based on Freeman-Durden decomposition (FDD) and a non-Gaussian\\n K-Wishart distribution classifier is proposed. This algorithm combines an advanced statistical distribution with spatial polarization scattering information of multi-looks PolSAR data. We use the prior probability characteristics of the Markov random field model to adaptively adjust\\n the cluster center to make the classification more accurate. The experiment result shows that the proposed algorithm based on non-Gaussian models can better retain the polarization information of the target and the clustering accuracy was effectively improved on the real SAR images.\",\"PeriodicalId\":16446,\"journal\":{\"name\":\"Journal of Nanoelectronics and Optoelectronics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nanoelectronics and Optoelectronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1166/jno.2023.3465\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nanoelectronics and Optoelectronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1166/jno.2023.3465","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

偏振合成孔径雷达(PolSAR)可以提供完整的地形偏振特性。地形分类是PolSAR数据最常见的应用。提出了一种基于Freeman-Durden分解(FDD)和非高斯K -Wishart分布分类器的无监督聚类算法。该算法将多视点PolSAR数据的高级统计分布与空间极化散射信息相结合。我们利用马尔可夫随机场模型的先验概率特征自适应调整聚类中心,使分类更加准确。实验结果表明,基于非高斯模型的聚类算法能更好地保留目标的极化信息,在真实SAR图像上有效提高了聚类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised Classification of Non-Gaussian Terrain Based on Polarimetric Decomposition
Polarimetric synthetic aperture radar (PolSAR) can provide complete polarization property of terrain. Terrain classification is the most common application of PolSAR data. In this paper, an unsupervised clustering algorithm based on Freeman-Durden decomposition (FDD) and a non-Gaussian K-Wishart distribution classifier is proposed. This algorithm combines an advanced statistical distribution with spatial polarization scattering information of multi-looks PolSAR data. We use the prior probability characteristics of the Markov random field model to adaptively adjust the cluster center to make the classification more accurate. The experiment result shows that the proposed algorithm based on non-Gaussian models can better retain the polarization information of the target and the clustering accuracy was effectively improved on the real SAR images.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Nanoelectronics and Optoelectronics
Journal of Nanoelectronics and Optoelectronics 工程技术-工程:电子与电气
自引率
16.70%
发文量
48
审稿时长
12.5 months
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信