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dc.contributor.authorPenna B
dc.contributor.authorTillo T
dc.contributor.authorMagli E
dc.contributor.authorOlmo G
dc.contributor.editor
dc.date.accessioned2018-05-16T13:17:20Z
dc.date.available2018-05-16T13:17:20Z
dc.date.issued2005
dc.identifier.isbn978-0-7803-9050-8
dc.identifier.urihttp://dx.doi.org/10.1109/IGARSS.2005.1526124
dc.identifier.urihttp://ieeexplore.ieee.org/abstract/document/1526124/
dc.identifier.urihttp://hdl.handle.net/10863/4649
dc.description.abstractHyperspectral image compression has recently attracted a remarkable interest for remote sensing applications. In this paper we propose a unified embedded lossy-to-lossless compression framework based on the JPEG 2000 standard. In particular, we exploit the multicomponent transformation feature of Part 2 of JPEG 2000 to devise a compression framework based on a spectral decorrelating transform followed by JPEG 2000 compression of the transformed coefficients. We evaluate several possible choices for the spectral transform, including a floating-point DCT, an integer DCT, and a wavelet transform. The final version of the proposed algorithm has been compared to 3D-SPIHT in the lossy case, and to several state-of-the-art compression algorithms including JPEG-LS and 3D-CALIC in the lossless case. Experimental results on AVIRIS data show that the proposed technique exhibits very competitive performance for both reversible and irreversible compression, with significantly lower complexity than DPCM-based methods, and memory requirements compatible with typical onboard processing subsystems of remote sensing platforms.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights
dc.titleEmbedded lossy to lossless compression of hyperspectral images using JPEG 2000en_US
dc.typeBook chapteren_US
dc.date.updated2018-05-15T12:07:49Z
dc.publication.title2005 IEEE International Geoscience and Remote Sensing Symposium; IGARSS '05 ; Proceedings
dc.language.isiEN-GB
dc.description.fulltextreserveden_US


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