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dc.contributor.authorMolero, Jose M.
dc.contributor.authorGarzon, E.M.
dc.contributor.authorGarcía, I.
dc.contributor.authorQuintana Ortí, Enrique S.
dc.contributor.authorPlaza, Antonio
dc.date.accessioned2015-09-21T17:00:12Z
dc.date.available2015-09-21T17:00:12Z
dc.date.issued2014
dc.identifier.issn1939-1404
dc.identifier.urihttp://hdl.handle.net/10234/133485
dc.description.abstractAnomaly detection is an important task for hyperspectral data exploitation. Although many algorithms have been developed for this purpose in recent years, due to the large dimensionality of hyperspectral image data, fast anomaly detection remains a challenging task. In this work, we exploit the computational power of commodity graphics processing units (GPUs) and multicore processors to obtain implementations of a well-known anomaly detection algorithm developed by Reed and Xiaoli (RX algorithm), and a local (LRX) variant, which basically consists in applying the same concept to a local sliding window centered around each image pixel. LRX has been shown to be more accurate to detect small anomalies but computationally more expensive than RX. Our interest is focused on improving the computational aspects, not only through efficient parallel implementations, but also by analyzing the mathematical issues of the method and adopting computationally inexpensive solvers. Futhermore, we also assess the energy consumption of the newly developed parallel implementations, which is very important in practice. Our optimizations (based on software and hardware techniques) result in a significant reduction of execution time and energy consumption, which are keys to increase the practical interest of the considered algorithms. Indeed, for RX, the runtime obtained is less than the data acquisition time when real hyperspectral images are used. Our experimental results also indicate that the proposed optimizations and the parallelization techniques can significantly improve the general performance of the RX and LRX algorithms while retaining their anomaly detection accuracy.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherIEEEca_CA
dc.relation.isPartOfSelected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 2014, vol. 7, nº 6, p. 2256-2266ca_CA
dc.rights"(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."ca_CA
dc.subjectanomaly detectionca_CA
dc.subjectenergy consumptionca_CA
dc.subjectgraphics processing units (GPUs)ca_CA
dc.subjecthyperspectral imagingca_CA
dc.subjectmulticore processorsca_CA
dc.titleEfficient Implementation of Hyperspectral Anomaly Detection Techniques on GPUs and Multicore Processorsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1109/JSTARS.2014.2328614
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6851148&filter%3DAND%28p_IS_Number%3A6870503%29%26pageNumber%3D2ca_CA


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