Infrared Small Target Detection Algorithm Based on Robust Tensor Decomposition Model within Bayesian Framework

Yihua Tan, Zhi Li, Yuan Xiao, Na Liu
{"title":"Infrared Small Target Detection Algorithm Based on Robust Tensor Decomposition Model within Bayesian Framework","authors":"Yihua Tan, Zhi Li, Yuan Xiao, Na Liu","doi":"10.1109/IGARSS.2019.8900369","DOIUrl":null,"url":null,"abstract":"Small targets detection in infrared video can be further improved by considering that the background has high correlation and low rank characteristics while foreground objects maintain sparsity. In this paper, a new infrared small target detection algorithm within Bayesian framework is proposed. A three-dimensional tensor structure of the video sequence is supposed to be decomposed into low rank background, sparse foreground and noise. The corresponding probabilistic models for the three parts form a Bayesian network which is solved by using variational Bayesian inference. Finally, the isolated sparse component is utilized for further target detecition. Experimental results show that the proposed method is suitable for the detection of small infrared target with good detection accuracy and robustness.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"35 1","pages":"1160-1163"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2019.8900369","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Small targets detection in infrared video can be further improved by considering that the background has high correlation and low rank characteristics while foreground objects maintain sparsity. In this paper, a new infrared small target detection algorithm within Bayesian framework is proposed. A three-dimensional tensor structure of the video sequence is supposed to be decomposed into low rank background, sparse foreground and noise. The corresponding probabilistic models for the three parts form a Bayesian network which is solved by using variational Bayesian inference. Finally, the isolated sparse component is utilized for further target detecition. Experimental results show that the proposed method is suitable for the detection of small infrared target with good detection accuracy and robustness.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于贝叶斯框架鲁棒张量分解模型的红外小目标检测算法
考虑到背景具有高相关性和低秩的特点,而前景目标保持稀疏性,可以进一步提高红外视频中的小目标检测。本文提出了一种基于贝叶斯框架的红外小目标检测算法。将视频序列的三维张量结构分解为低秩背景、稀疏前景和噪声。三部分对应的概率模型组成一个贝叶斯网络,用变分贝叶斯推理求解。最后,利用分离的稀疏分量进一步检测目标。实验结果表明,该方法适用于红外小目标的检测,具有较好的检测精度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Visual Question Answering From Remote Sensing Images The Impact of Additive Noise on Polarimetric Radarsat-2 Data Covering Oil Slicks Edge-Convolution Point Net for Semantic Segmentation of Large-Scale Point Clouds Burn Severity Estimation in Northern Australia Tropical Savannas Using Radiative Transfer Model and Sentinel-2 Data The Truth About Ground Truth: Label Noise in Human-Generated Reference Data
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1