http://www.cnr.it/ontology/cnr/individuo/prodotto/ID80435
Low-complexity lossless/near-lossless compression of hyperspectral imagery through classified linear spectral prediction (Contributo in atti di convegno)
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- Low-complexity lossless/near-lossless compression of hyperspectral imagery through classified linear spectral prediction (Contributo in atti di convegno) (literal)
- Anno
- 2005-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/IGARSS.2005.1526122 (literal)
- Alternative label
B. Aiazzi, L. Alparone, S. Baronti, C. Lastri, L. Santurri (2005)
Low-complexity lossless/near-lossless compression of hyperspectral imagery through classified linear spectral prediction
in IGARSS 2005, Seoul - Korea, 25-29 July 2005
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- B. Aiazzi, L. Alparone, S. Baronti, C. Lastri, L. Santurri (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
- Proc. IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, 2529 Jul. 2005, Vol. 1, pp. 132-135 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1526122 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- proceedings of IEEE IGARSS 2005 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Note
- ISI Web of Science (WOS) (literal)
- Scopu (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- B. Aiazzi,, S. Baronti, C. Lastri, L. Santurri: IFAC-CNR.
L. Alparone: DET università degli studi di Firenze (literal)
- Titolo
- Low-complexity lossless/near-lossless compression of hyperspectral imagery through classified linear spectral prediction (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 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. © 2005 IEEE. (literal)
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