Hyperspectral images can be efficiently compressed through a linear predictive model, as for example the one used in the SLSQ algorithm. In this paper we exploit this predictive model on the AVIRIS images by individuating, through an off-line approach, a common subset of bands, which are not spectrally related with any other bands. These bands are not useful as prediction reference for the SLSQ 3-D predictive model and we need to encode them via other prediction strategies which consider only spatial correlation. We have obtained this subset by clustering the AVIRIS bands via the clustering by compression approach. The main result of this paper is the list of the bands, not related with the others, for AVIRIS images. The clustering trees obtained for AVIRIS and the relationship among bands they depict is also an interesting starting point for future research.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral Images
PIZZOLANTE, RAFFAELE;CARPENTIERI, Bruno
2014-01-01
Abstract
Hyperspectral images can be efficiently compressed through a linear predictive model, as for example the one used in the SLSQ algorithm. In this paper we exploit this predictive model on the AVIRIS images by individuating, through an off-line approach, a common subset of bands, which are not spectrally related with any other bands. These bands are not useful as prediction reference for the SLSQ 3-D predictive model and we need to encode them via other prediction strategies which consider only spatial correlation. We have obtained this subset by clustering the AVIRIS bands via the clustering by compression approach. The main result of this paper is the list of the bands, not related with the others, for AVIRIS images. The clustering trees obtained for AVIRIS and the relationship among bands they depict is also an interesting starting point for future research.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
