Linear Data Compression of Hyperspectral Images

Kaori Tanji, Hayato Itoh, Atsushi Imiya, Naohiro Manago, Hiroaki Kuze; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3001-3007


The aim of the paper is to analyse hyperspectral images using tensor principal component analysis of multi-way data sets. Because of high-resolution sampling in the colour channels, images observed by a hyperspectral camera system are expressed by three-mode tensors. The Tucker-3 decomposition of a three-mode tensor is used in behaviourmetry and psychology for the extraction of relations among three entries as an extension of the usual principal component analysis for statistical analysis. Hyperspectral images express spectral information of two-dimensional images on the imaging plane. Therefore, for statistical analysis, we adopt the Tucker-3 decomposition. The Tucker-3 decomposition of hyperspectral images extracts statistically dominant information from hyperspectral images. Tensor principal component analysis allows us to extract dominant light-channel information from hyperspectral images.

Related Material

author = {Tanji, Kaori and Itoh, Hayato and Imiya, Atsushi and Manago, Naohiro and Kuze, Hiroaki},
title = {Linear Data Compression of Hyperspectral Images},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}