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HYPERSPECTRAL IMAGE COMPRESSION

dc.contributor.authorHallidy, William H., Jr.,
dc.contributor.authorDoerr, Michael
dc.date.accessioned2016-05-09T20:46:18Zen
dc.date.available2016-05-09T20:46:18Zen
dc.date.issued1999-10en
dc.identifier.issn0884-5123en
dc.identifier.issn0074-9079en
dc.identifier.urihttp://hdl.handle.net/10150/608744en
dc.descriptionInternational Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevadaen_US
dc.description.abstractSystems & Processes Engineering Corporation (SPEC) compared compression and decompression algorithms and developed optimal forms of lossless and lossy compression for hyperspectral data. We examined the relationship between compression-induced distortion and additive noise, determined the effect of errors on the compressed data, and showed that the data could separate targets from clutter after more than 50:1 compression.
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.language.isoen_USen
dc.publisherInternational Foundation for Telemeteringen
dc.relation.urlhttp://www.telemetry.org/en
dc.rightsCopyright © International Foundation for Telemeteringen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectCompressionen
dc.subjectRiceen
dc.subjectWaveleten
dc.subjectHyperspectralen
dc.subjectTarget/Clutteren
dc.subjectBit Erroren
dc.titleHYPERSPECTRAL IMAGE COMPRESSIONen_US
dc.typetexten
dc.typeProceedingsen
dc.contributor.departmentSystems & Processes Engineering Corporationen
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.en
refterms.dateFOA2018-04-25T14:42:25Z
html.description.abstractSystems & Processes Engineering Corporation (SPEC) compared compression and decompression algorithms and developed optimal forms of lossless and lossy compression for hyperspectral data. We examined the relationship between compression-induced distortion and additive noise, determined the effect of errors on the compressed data, and showed that the data could separate targets from clutter after more than 50:1 compression.


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