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Comprehensive review of hyperspectral image compression algorithms

dc.contributor.authorDua, Y.
dc.contributor.authorKumar, V.
dc.contributor.authorSingh, R.S.
dc.date.accessioned2020-11-26T10:30:28Z
dc.date.available2020-11-26T10:30:28Z
dc.date.issued2020-09-01
dc.description.abstractRapid advancement in the development of hyperspectral image analysis techniques has led to specialized hyperspectral missions. It results in the bulk transmission of hyperspectral images from sensors to analysis centers and finally to data centers. Storage of these large size images is a critical issue that is handled by compression techniques. This survey focuses on different hyperspectral image compression algorithms that have been classified into two broad categories based on eight internal and six external parameters. In addition, we identified research challenges and suggested future scope for each technique. The detailed classification used in this paper can categorize other compression algorithms and may help in selecting research objectives. © 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).en_US
dc.identifier.issn00913286
dc.identifier.urihttps://idr-sdlib.iitbhu.ac.in/handle/123456789/1016
dc.language.isoen_USen_US
dc.publisherSPIEen_US
dc.relation.ispartofseriesOptical Engineering;Vol. 59 Issue 9
dc.subjecthyperspectral image compressionen_US
dc.subjectmachine learningen_US
dc.subjectdeep learningen_US
dc.subjecttensor decompositionen_US
dc.subjectlossy compressionen_US
dc.subjectlossless compressionen_US
dc.titleComprehensive review of hyperspectral image compression algorithmsen_US
dc.typeArticleen_US

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