A Comparative Analysis of Tensor Decomposition Models Using Hyper Spectral Image
This work addresses the challenge of efficiently processing hyper spectral data for applications like material identification, though it is incremental as it compares existing methods.
The paper tackled the problem of selecting the best tensor decomposition model for analyzing hyper spectral images, finding that Block Term Decomposition (BTD) outperformed LMLRA and CPD in decomposing the data into factor matrices.
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.