Comprehensive review of hyperspectral image compression algorithms

Show simple item record

dc.contributor.author Dua, Y.
dc.contributor.author Kumar, V.
dc.contributor.author Singh, R.S.
dc.date.accessioned 2020-11-26T10:30:28Z
dc.date.available 2020-11-26T10:30:28Z
dc.date.issued 2020-09-01
dc.identifier.issn 00913286
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/1016
dc.description.abstract Rapid 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.language.iso en_US en_US
dc.publisher SPIE en_US
dc.relation.ispartofseries Optical Engineering;Vol. 59 Issue 9
dc.subject hyperspectral image compression en_US
dc.subject machine learning en_US
dc.subject deep learning en_US
dc.subject tensor decomposition en_US
dc.subject lossy compression en_US
dc.subject lossless compression en_US
dc.title Comprehensive review of hyperspectral image compression algorithms en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search in IDR


Advanced Search

Browse

My Account