Band Clustering for the Lossless Compression of
AVIRIS Hyperspectral Images
R. Pizzolante, and B. Carpentieri. ACEEE International Journal of Signal and Image Processing, 5 (1):
14(January 2014)
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.
%0 Journal Article
%1 pizzolante2014clustering
%A Pizzolante, Raffaele
%A Carpentieri, Bruno
%D 2014
%E Das, Dr. Vinu V
%J ACEEE International Journal of Signal and Image Processing
%K band_clustering band_ordering
%N 1
%P 14
%T Band Clustering for the Lossless Compression of
AVIRIS Hyperspectral Images
%U http://searchdl.org/public/journals/2014/IJSIP/5/1/1532.pdf
%V 5
%X 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.
@article{pizzolante2014clustering,
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.},
added-at = {2014-02-13T10:37:19.000+0100},
author = {Pizzolante, Raffaele and Carpentieri, Bruno},
biburl = {https://www.bibsonomy.org/bibtex/2bd56afeb333dd2864801ef7c80fbf922/ideseditor},
editor = {Das, Dr. Vinu V},
interhash = {17bcce08a572d95fdc9d92679193956f},
intrahash = {bd56afeb333dd2864801ef7c80fbf922},
journal = {ACEEE International Journal of Signal and Image Processing},
keywords = {band_clustering band_ordering},
month = {January},
number = 1,
pages = 14,
timestamp = {2014-02-13T10:37:19.000+0100},
title = {Band Clustering for the Lossless Compression of
AVIRIS Hyperspectral Images},
url = {http://searchdl.org/public/journals/2014/IJSIP/5/1/1532.pdf},
volume = 5,
year = 2014
}