稠密城区SAR层析成像的相似准则

Clement Rambour, L. Denis, F. Tupin, J. Nicolas, H. Oriot, L. Ferro-Famil, C. Deledalle
{"title":"稠密城区SAR层析成像的相似准则","authors":"Clement Rambour, L. Denis, F. Tupin, J. Nicolas, H. Oriot, L. Ferro-Famil, C. Deledalle","doi":"10.1109/IGARSS.2017.8127315","DOIUrl":null,"url":null,"abstract":"Starting from a stack of co-registered SAR images in interferometric configuration, SAR tomography performs a reconstruction of the reflectivity of scatterers in 3-D. Several scatterers observed within the same resolution cell of each SAR image can be separated by jointly unmixing the SAR complex amplitude observed throughout the stack. To achieve a reliable tomographic reconstruction, it is necessary to estimate locally the SAR covariance matrix by performing some spatial averaging. This necessary averaging step introduces some resolution loss and can bias the tomographic reconstruction by mistakenly including the response of scatterers located within the averaging area but outside the resolution cell of interest. This paper addresses the problem of identifying pixels corresponding to similar tomographic content, i.e., pixels that can be safely averaged prior to tomographic reconstruction. We derive a similarity criterion adapted to SAR tomography and compare its performance with existing criteria on a stack of Spotlight TerraSAR-X images.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"70 1","pages":"1760-1763"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Similarity criterion for SAR tomography over dense urban area\",\"authors\":\"Clement Rambour, L. Denis, F. Tupin, J. Nicolas, H. Oriot, L. Ferro-Famil, C. Deledalle\",\"doi\":\"10.1109/IGARSS.2017.8127315\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Starting from a stack of co-registered SAR images in interferometric configuration, SAR tomography performs a reconstruction of the reflectivity of scatterers in 3-D. Several scatterers observed within the same resolution cell of each SAR image can be separated by jointly unmixing the SAR complex amplitude observed throughout the stack. To achieve a reliable tomographic reconstruction, it is necessary to estimate locally the SAR covariance matrix by performing some spatial averaging. This necessary averaging step introduces some resolution loss and can bias the tomographic reconstruction by mistakenly including the response of scatterers located within the averaging area but outside the resolution cell of interest. This paper addresses the problem of identifying pixels corresponding to similar tomographic content, i.e., pixels that can be safely averaged prior to tomographic reconstruction. We derive a similarity criterion adapted to SAR tomography and compare its performance with existing criteria on a stack of Spotlight TerraSAR-X images.\",\"PeriodicalId\":6466,\"journal\":{\"name\":\"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"volume\":\"70 1\",\"pages\":\"1760-1763\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2017.8127315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2017.8127315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

从干涉配置的共配准SAR图像堆栈开始,SAR层析成像在三维中重建散射体的反射率。在每个SAR图像的相同分辨率单元内观测到的多个散射体可以通过在整个叠加中观测到的SAR复振幅联合解混来分离。为了实现可靠的层析重建,有必要通过进行一些空间平均来局部估计SAR协方差矩阵。这个必要的平均步骤引入了一些分辨率损失,并且由于错误地包括位于平均区域内但在感兴趣的分辨率单元之外的散射体的响应,可能会使层析重建产生偏差。本文解决了识别与相似层析内容对应的像素的问题,即在层析重建之前可以安全地平均像素。我们推导了一个适用于SAR层析成像的相似标准,并将其性能与现有的聚光灯TerraSAR-X图像堆栈标准进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Similarity criterion for SAR tomography over dense urban area
Starting from a stack of co-registered SAR images in interferometric configuration, SAR tomography performs a reconstruction of the reflectivity of scatterers in 3-D. Several scatterers observed within the same resolution cell of each SAR image can be separated by jointly unmixing the SAR complex amplitude observed throughout the stack. To achieve a reliable tomographic reconstruction, it is necessary to estimate locally the SAR covariance matrix by performing some spatial averaging. This necessary averaging step introduces some resolution loss and can bias the tomographic reconstruction by mistakenly including the response of scatterers located within the averaging area but outside the resolution cell of interest. This paper addresses the problem of identifying pixels corresponding to similar tomographic content, i.e., pixels that can be safely averaged prior to tomographic reconstruction. We derive a similarity criterion adapted to SAR tomography and compare its performance with existing criteria on a stack of Spotlight TerraSAR-X 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
自引率
0.00%
发文量
0
×
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