Hyperspectral imaging has been widely studied in many applications; notably in
climate changes, vegetation, and desert studies. However, such kind of imaging brings a huge
amount of data, which requires transmission, processing, and storage resources for both
airborne and spaceborne imaging. Compression of hyperspectral data cubes is an effective
solution for these problems. Lossless compression of the hyperspectral data usually results in
low compression ratio, which may not meet the available resources; on the other hand, lossy
compression may give the desired ratio, but with a significant degradation effect on object
identification performance of the hyperspectral data. Moreover, most hyperspectral data
compression techniques exploits the similarities in spectral dimensions; which requires bands
reordering or regrouping, to make use of the spectral redundancy. In this paper, we analyze
the spectral cross correlation between bands for AVIRIS and Hyperion hyperspectral data;
spectral cross correlation matrix is calculated, assessing the strength of the spectral matrix, we
propose new technique to find highly correlated groups of bands in the hyperspectral data
cube based on "inter band correlation square", and finally, we propose a new technique of
band regrouping based on correlation values weights for different group of bands as network
of correlation.
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