http://www.cnr.it/ontology/cnr/individuo/prodotto/ID80779
On-Board Lossless Hyperspectral Data Compression: LUT-Based or Classified Spectral Prediction? (Contributo in atti di convegno)
- Type
- Label
- On-Board Lossless Hyperspectral Data Compression: LUT-Based or Classified Spectral Prediction? (Contributo in atti di convegno) (literal)
- Anno
- 2008-01-01T00:00:00+01:00 (literal)
- Alternative label
L. Santurri, B. Aiazzi, L. Alparone, S. Baronti, C. Lastri (2008)
On-Board Lossless Hyperspectral Data Compression: LUT-Based or Classified Spectral Prediction?
in On-Board Payload Data Compression Workshop, ESA/ESTEC, Noordwijk, The Neth, 26-27 Jun. 2008
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- L. Santurri, B. Aiazzi, L. Alparone, S. Baronti, C. Lastri (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- On-Board Payload Data Compression Workshop OPBDC-2008 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- In Proc. ESA OBPDC 2008, On-Board Payload Data Compression Workshop, ESA/ESTEC, Noordwijk, The Netherlands, 26-27 Jun. 2008. (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- L. Santurri, B. Aiazzi, S. Baronti, C. Lastri: IFAC-CNR.
L. Alparone: DET Università degli studi di Firenze. (literal)
- Titolo
- On-Board Lossless Hyperspectral Data Compression: LUT-Based or Classified Spectral Prediction? (literal)
- Abstract
- This paper presents a novel algorithm suitable for the lossless compression of hyperspectral imagery. The algorithm
generalises two previous algorithms, in which the concept nearest neighbour (NN) prediction implemented through
lookup tables (LUTs) was introduced. Here, the set of LUTs, two or more, say M, on each band are allowed to span
more than one previous band, say N bands, and the decision among one of the NM possible prediction values is based
on the closeness of the value contained in the LUT to an advanced prediction, spanning N previous bands as well,
provided by a top-performing scheme recently developed by the authors and featuring a classified spectral prediction.
Experimental results carried out on the AVIRIS '97 data-set show improvements up to 15% over the baseline LUT-NN
algorithm. However, preliminary results carried out on raw data show that all LUT-based methods are not suitable for
on-board compression, since they take advantage uniquely of the sparseness of data histograms, which is originated by
the on-ground calibration procedure. (literal)
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