  Hyper spectral imaging is a remote sensing technology, providing variety of
applications such as material identification, space object identification,
planetary exploitation etc. It deals with capturing continuum of images of the
earth surface from different angles. Due to the multidimensional nature of the
image, multi-way arrays are one of the possible solutions for analyzing hyper
spectral data. This multi-way array is called tensor. Our approach deals with
implementing three decomposition models LMLRA, BTD and CPD to the sample data
for choosing the best decomposition of the data set. The results have proved
that Block Term Decomposition (BTD) is the best tensor model for decomposing
the hyper spectral image in to resultant factor matrices.
