{"title":"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","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":"{\"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}","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}
Infrared Small Target Detection Algorithm Based on Robust Tensor Decomposition Model within Bayesian Framework
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.