Unmixing hyperspectral data by using signal subspace sampling

Jakob Spiegelberg; Shunsuke Muto; Masahiro Ohtsuka; Kristiaan Pelckmans; Ján Rusz

doi:10.1016/j.ultramic.2017.07.009

Unmixing hyperspectral data by using signal subspace sampling

Ultramicroscopy. 2017 Nov:182:205-211. doi: 10.1016/j.ultramic.2017.07.009. Epub 2017 Jul 10.

Authors

Jakob Spiegelberg¹, Shunsuke Muto², Masahiro Ohtsuka³, Kristiaan Pelckmans⁴, Ján Rusz⁵

Affiliations

¹ Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden. Electronic address: jakob.spiegelberg@physics.uu.se.
² Advanced Measurement Technology Center, Institute of Materials and Systems for Sustainability, Nagoya University, Chikusa-ku, Nagoya 464-8603, Japan.
³ Graduate School of Engineering, Nagoya University, Chikusa-ku, Nagoya 464-8603, Japan.
⁴ Department of Information Technology, Uppsala University, Box 337, S-751 05 Uppsala, Sweden.
⁵ Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.

PMID: 28711769
DOI: 10.1016/j.ultramic.2017.07.009

Abstract

This paper demonstrates how Signal Subspace Sampling (SSS) is an effective pre-processing step for Non-negative Matrix Factorization (NMF) or Vertex Component Analysis (VCA). The approach allows to uniquely extract non-negative source signals which are orthogonal in at least one observation channel, respectively. It is thus well suited for processing hyperspectral images from X-ray microscopy, or other emission spectroscopies, into its non-negative source components. The key idea is to resample the given data so as to satisfy better the necessity and sufficiency conditions for the subsequent NMF or VCA. Results obtained both on an artificial simulation study as well as based on experimental data from electron-microscopy are reported.

Publication types

Research Support, Non-U.S. Gov't