A Comparative Analysis of Tensor Decomposition Models Using Hyper Spectral Image

TL;DR

This paper compares tensor decomposition models applied to hyper spectral images, demonstrating that Block Term Decomposition (BTD) provides the most effective data representation for this type of multidimensional remote sensing data.

Contribution

It evaluates and compares three tensor decomposition models on hyper spectral data, identifying BTD as the most suitable for this application.

Findings

BTD outperforms LMLRA and CPD in decomposing hyper spectral images.

BTD produces more accurate and meaningful factor matrices.

The study guides the selection of tensor models for hyper spectral data analysis.

Abstract

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.