基于贝叶斯最小风险鲁棒正则化技术的遥感特征场重建

Y. Shkvarko, I. Villalón-Turrubiates, J.L. Leyva-Montiel
{"title":"基于贝叶斯最小风险鲁棒正则化技术的遥感特征场重建","authors":"Y. Shkvarko, I. Villalón-Turrubiates, J.L. Leyva-Montiel","doi":"10.1109/CAMSAP.2007.4498009","DOIUrl":null,"url":null,"abstract":"The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. The problem-oriented modification of the previously proposed fused Bayesian-regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill-poseness due to system-level and model-level uncertainties. We report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Remote Sensing Signature Fields Reconstruction Via Robust Regularization of Bayesian Minimum Risk Technique\",\"authors\":\"Y. Shkvarko, I. Villalón-Turrubiates, J.L. Leyva-Montiel\",\"doi\":\"10.1109/CAMSAP.2007.4498009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. The problem-oriented modification of the previously proposed fused Bayesian-regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill-poseness due to system-level and model-level uncertainties. We report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.\",\"PeriodicalId\":220687,\"journal\":{\"name\":\"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMSAP.2007.4498009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMSAP.2007.4498009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

针对大规模传感器阵列雷达/合成孔径雷达增强遥感的需要,开发了高分辨率重建成像和场景分析的鲁棒数值技术。对先前提出的融合贝叶斯正则化(FBR)增强雷达成像方法进行了面向问题的改进,使其能够重建感兴趣的遥感特征(RSS),减轻了由于系统级和模型级不确定性导致的问题不适。我们报告了从增强的现实世界环境图像中重建水文RSS的一些模拟结果,表明了所开发方法的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Remote Sensing Signature Fields Reconstruction Via Robust Regularization of Bayesian Minimum Risk Technique
The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. The problem-oriented modification of the previously proposed fused Bayesian-regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill-poseness due to system-level and model-level uncertainties. We report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.
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