Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection

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dc.contributor.authorChang, Chein-I
dc.contributor.authorCao, Hongju
dc.contributor.authorSong, Meiping
dc.date.accessioned2022-11-09T18:02:22Z
dc.date.available2022-11-09T18:02:22Z
dc.date.issued2021-03-25
dc.description.abstractOrthogonal subspace projection (OSP) is a versatile hyperspectral imaging technique which has shown great potential in dimensionality reduction, target detection, spectral unmixing, etc. However, due to its inherent requirement of prior target knowledge, OSP has not been explored in anomaly detection. This article takes advantage of an unsupervised OSP-based algorithm, automatic target generation process (ATGP), and a recently developed OSP-go decomposition (OSP-GoDec) along with data sphering (DS) to make OSP applicable to anomaly detection. Its idea is to implement ATGP on the background (BKG) and target subspaces constructed from the low-rank matrix L and sparse matrix S generated by OSP-GoDec to derive an OSP-based anomaly detector (OSP-AD). In particular, OSP-AD also includes DS to remove BKG interference from the target subspace so as to enhance anomaly detection. Surprisingly, operating data samples on different constructions of the BKG subspace and the target subspace yields various versions of OSP-AD. Experiments show that given an appropriate construction of the BKG subspace and the target subspace, OSP-AD can be shown to outperform existing anomaly detectors including Reed-Xiaoli anomaly detector and collaborative representation-based anomaly detector (CRD).en_US
dc.description.sponsorshipThe work of Chein-I Chang was supported by the Fundamental Research Funds for Central Universities under Grant 3132019341. The work of Hongju Cao was supported by the Nature Science Foundation of Liaoning Province under Grant 20180550018. The work of Meiping Song was supported by the National Nature Science Foundation of China under Grant 61971082, Grant 61890964, and Grant 3132019341.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9387095en_US
dc.format.extent18 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2v7pv-kmrf
dc.identifier.citationC. -I. Chang, H. Cao and M. Song, "Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 4915-4932, 2021, doi: 10.1109/JSTARS.2021.3068983.en_US
dc.identifier.urihttps://doi.org/10.1109/JSTARS.2021.3068983
dc.identifier.urihttp://hdl.handle.net/11603/26281
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleOrthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detectionen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-5450-4891en_US

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