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Parallel hyperspectral coded aperture for compressive sensing on GPUs

2016-02Journal article Restricted access
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dc.contributor.authorBernabé, Sérgio
dc.contributor.authorMartin, Gabriel
dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.contributor.authorPlaza, António
dc.contributor.authorSilva, Vítor
dc.date.accessioned2016-04-27T14:45:37Z
dc.date.available2016-04-27T14:45:37Z
dc.date.issued2016-02
dc.description.abstractThe application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.pt_PT
dc.identifier.citationBERNABÉ, Sérgio; [et al] - Parallel hyperspectral coded aperture for compressive sensing on GPUs. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. ISSN 1939-1404. Vol. 9, N.º 2 (2016). pp. 932-944pt_PT
dc.identifier.doi10.1109/JSTARS.2015.2436440pt_PT
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.urihttp://hdl.handle.net/10400.21/6098
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEE - Institute of Electrical and Electronics Engineers Inc.pt_PT
dc.relationNEW HYPERSPECTRAL COMPRESSIVE SENSING AND UNMIXING FRAMEWORK.
dc.subjectCoded aperture compressive sensingpt_PT
dc.subjectCSpt_PT
dc.subjectGraphics processing unitspt_PT
dc.subjectGPUspt_PT
dc.subjectHigh-performance computing hyperspectral imagingpt_PT
dc.titleParallel hyperspectral coded aperture for compressive sensing on GPUspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleNEW HYPERSPECTRAL COMPRESSIVE SENSING AND UNMIXING FRAMEWORK.
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F50008%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBPD%2F94160%2F2013/PT
oaire.citation.endPage944pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage932pt_PT
oaire.citation.titleIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensingpt_PT
oaire.citation.volume9pt_PT
oaire.fundingStream5876
person.familyNameNascimento
person.familyNameDias da Silva
person.givenNameJose
person.givenNameVítor
person.identifier.ciencia-id6912-6F61-1964
person.identifier.ciencia-idA912-8252-99D4
person.identifier.orcid0000-0002-5291-6147
person.identifier.orcid0000-0002-1862-1230
person.identifier.ridE-6212-2015
person.identifier.scopus-author-id55920018000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationd0763cf1-6a95-4270-9428-53ea86b8cf35
relation.isAuthorOfPublication.latestForDiscoveryd0763cf1-6a95-4270-9428-53ea86b8cf35
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