http://www.cnr.it/ontology/cnr/individuo/prodotto/ID80688

Near-lossless compression of hyperspectral data through classified spectral prediction (Contributo in atti di convegno)

Type

Label

Near-lossless compression of hyperspectral data through classified spectral prediction (Contributo in atti di convegno) (literal)

Anno

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

Alternative label

B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, C. Lastri, L. Santurri (2005)
Near-lossless compression of hyperspectral data through classified spectral prediction
in SPIE 50th Annual Meeting, San Diego, 31 July - 04 August 2005
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, C. Lastri, L. Santurri (literal)

Pagina inizio

Pagina fine

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

proceedings of SPIE 50th Annual Meeting, Mathematics of data/image coding, compression, and enchryption VIII, with applications (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

SPIE 50th Annual Meeting, San Diego, CA, 31 Jul.-4 Aug. 2005, SPIE Vol. 5915, Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications, M. S. Schmalz (Ed.), 59150M, Sep. 2005 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

Note

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

B. Aiazzi, S. Baronti, C. Lastri, L. Santurri: IFAC - CNR L.Alparone: Dept. Electron. & Telecoms., University of Florence, Via Santa Marta, 3, 50139 Firenze, Italy A. Garzelli : Dept. Information Engineering, University of Siena, Via Roma, 56, 53100 Siena, Italy (literal)

Titolo

Near-lossless compression of hyperspectral data through classified spectral prediction (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

Abstract

This paper presents a novel scheme for lossless/near-lossless hyperspectral image compression, that exploits a classified spectral prediction. MMSE spectral predictors are calculated for small spatial blocks of each band and are classified (clustered) to yield a user-defined number of prototype predictors for each wavelength, capable of matching the spatial features of different classes of pixel spectra. Unlike most of the literature, the proposed method employs a purely spectral prediction, that is suitable for compressing the data in band-interleaved-by-line (BIL) format, as they are available at the output of the on-board spectrometer. In that case, the training phase, i.e., clustering of predictors for each wavelength, may be moved off-line. Thus, prediction will be slightly less fitting, but the overhead of predictors calculated on-line is saved. Although prediction is purely spectral, hence 1D, spatial correlation is removed by the training phase of predictors, aimed at finding statistically homogeneous spatial classes matching the set of prototype spectral predictors. Experimental results on AVIRIS data show improvements over the most advanced methods in the literature, with a computational complexity far lower than that of analogous methods by other authors. (literal)

Editore