Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection
Guo,Tan1; Luo,Fulin2; Zhang,Lei3; Zhang,Bob4; Tan,Xiaoheng3; Zhou,Xiaocheng5
2020
Source PublicationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ISSN1939-1404
Volume13Pages:3521-3533
AbstractExisting sparsity-based hyperspectral image (HSI) target detection methods have two key problems. 1) The background dictionary is locally constructed by the pixels between the inner and outer windows, surrounding and enclosing the central test pixel. The dual-window strategy is intricate and might result in impure background dictionary deteriorating the detection performance. 2) For an unbalanced binary classification problem, the target dictionary atoms are generally inadequate compared with the background dictionary, which might yield unstable performance. For the issues, this article proposes a novel structurally incoherent background and target dictionaries (SIBTD) learning model for HSI target detection. Specifically, with the concept that the observed HSI data is composed of low-rank background, sparsely distributed targets, and dense Gaussian noise, the background and target dictionaries can be jointly derived from the observed HSI data. Additionally, the introduction of structural incoherence can enhances the discrimination between the target and background dictionaries. Thus, the developed model can not only lead to a pure and unified background dictionary but also augment the target dictionary for improved detection performance. Besides, an efficient optimization algorithm is devised to solve SIBTD model, and the performance of SIBTD is verified on three benchmark HSI datasets in comparison with several state-of-the-art detectors.
KeywordDictionary decomposition hyperspectral image (HSI) low-rank constraint sparse model target detection
DOI10.1109/JSTARS.2020.3002549
URLView the original
Language英语
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Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing,China
2.Mapping and Remote Sensing,State Key Laboratory of Information Engineering in Surveying,Wuhan University,Wuhan,China
3.School of Microelectronics and Communications Engineering,Chongqing University,Chongqing,China
4.Department of Computer and Information Science,University of Macau,Macao
5.Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education,Fuzhou University,Fuzhou,China
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GB/T 7714
Guo,Tan,Luo,Fulin,Zhang,Lei,et al. Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020,13:3521-3533.
APA Guo,Tan,Luo,Fulin,Zhang,Lei,Zhang,Bob,Tan,Xiaoheng,&Zhou,Xiaocheng.(2020).Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,13,3521-3533.
MLA Guo,Tan,et al."Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13(2020):3521-3533.
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