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Hyperspectral compressive sensing - a low power consumption approach

2018Conference object Restricted access
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dc.contributor.authorNascimento, Jose
dc.contributor.authorVéstias, Mário
dc.contributor.authorDuarte, Rui
dc.date.accessioned2019-04-17T10:17:55Z
dc.date.available2019-04-17T10:17:55Z
dc.date.issued2018
dc.description.abstractHyperspectral imaging instruments allow data collection in hundreds of spectral bands for the same area on the surface of the Earth. The resulting multidimensional data cube typically comprises several GBs per light. Due to the extremely large volumes of data collected by imaging spectrometers, hyperspectral data compression, dimensionality reduction and Compressive Sensing (CS) techniques has received considerable interest in recent years. These data are usually acquired by a satellite or an airbone instrument and sent to a ground station on Earth for subsequent processing. Usually the bandwidth connection between the satellite/airborne platform and the ground station is reduced, which limits the amount of data that can be transmitted. As a result, there is a clear need for (either lossless or lossy) hyperspectral data compression techniques that can be applied on-board the imaging instrument. This paper, presents a study of the power and time consumption and accuracy of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform, which is well suited to perform vector operations such as dot products. This implementation exploits the architecture at low level, using shared memory and coalesced accesses to memory. The conducted experiments have been performed to demonstrate the applicability, in terms of accuracy, time consuming and power consumption of these methods for onboard processing. The results show that by using this low power consumption GPU is it possible to obtain real-time performance with a very limited power requirement.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationNASCIMENTO, José M. P.; VÉSTIAS, Mário; DUARTE, Rui – Hyperspectral compressive sensing - a low power consumption approach. In Proceedings of SPIE – High-Performance Computing in Geoscience and Remote Sensing VIII. Berlin, Germany: SPIE. ISSN 0277-786X. Vol. 10792, pp. 1079202-1- 1079202-10pt_PT
dc.identifier.doi10.1117/12.2326118pt_PT
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.urihttp://hdl.handle.net/10400.21/9872
dc.language.isoengpt_PT
dc.publisherSociety of Photo-optical Instrumentation Engineerspt_PT
dc.subjectConsumptionpt_PT
dc.subjectPowerpt_PT
dc.subjectConsumopt_PT
dc.subjectPoderpt_PT
dc.titleHyperspectral compressive sensing - a low power consumption approachpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceSep 12-13, 2018 - Berlin, Germanypt_PT
oaire.citation.endPage1079202-10pt_PT
oaire.citation.startPage1079202-1pt_PT
oaire.citation.titleConference on High-Performance Computing in Geoscience and Remote Sensing VIIIpt_PT
oaire.citation.volume10792pt_PT
person.familyNameNascimento
person.familyNameVéstias
person.familyNameDuarte
person.givenNameJose
person.givenNameMário
person.givenNameRui
person.identifier.ciencia-id6912-6F61-1964
person.identifier.ciencia-id4717-C2C7-3F2C
person.identifier.ciencia-idB91E-770F-19A3
person.identifier.orcid0000-0002-5291-6147
person.identifier.orcid0000-0001-8556-4507
person.identifier.orcid0000-0002-7060-4745
person.identifier.ridE-6212-2015
person.identifier.ridH-9953-2012
person.identifier.ridI-4402-2015
person.identifier.scopus-author-id55920018000
person.identifier.scopus-author-id14525867300
person.identifier.scopus-author-id24823991600
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isAuthorOfPublicationa7d22b29-c961-45ac-bc09-cd5e1002f1e8
relation.isAuthorOfPublicationf2b4b9e6-6c89-48c7-bc83-62d2e98a787b
relation.isAuthorOfPublication.latestForDiscoveryf2b4b9e6-6c89-48c7-bc83-62d2e98a787b

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