Title :
Dimension selective tensor compression of hyperspectral images
Author :
Rahimi, Mahdi Salmani ; Sodagari, Shabnam ; Avanaki, Alireza Nasiri
Author_Institution :
Univ. of Tehran, Tehran
Abstract :
An efficient method for hyperspectral image compression is presented using tensor approximation. Hyperspectral images are first modeled as 3D tensors. Every tensor is then represented using its Tucker representation and matrices for every mode are calculated. Choosing eigenvectors corresponding to greatest eigenvalues of projection matrices, we reach a lower order tensor. Our method not only exploits redundancies between bands but also uses spatial correlations of every band image and therefore, as simulation results applied to airborne visible/infrared imaging spectrometer (AVIRIS) files demonstrate, leads to a remarkable compression ratio and quality.
Keywords :
data compression; eigenvalues and eigenfunctions; image coding; matrix algebra; tensors; dimension selective tensor compression; eigenvalues; hyperspectral images; Hyperspectral imaging; Hyperspectral sensors; Image coding; Infrared imaging; Infrared spectra; Layout; Multidimensional systems; Principal component analysis; Spectroscopy; Tensile stress;
Conference_Titel :
Student Paper, 2008 Annual IEEE Conference
Conference_Location :
Aalborg
Print_ISBN :
978-1-4244-2156-5
DOI :
10.1109/AISPC.2008.4460554