Joint selection of essential pixels and essential variables across hyperspectral images

An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly recover all the information available in the rows and columns of a data set. Working with hyperspectral images, this approach translates into the selection of essential spectral pixels (ESPs) and essential spatial variables (ESVs). This results in a highly-reduced data set, the benefits of which can be minimized computational effort, meticulous data mining, easier model building as well as better problem understanding or interpretation. Working with both simulated and real data, we show that (i) reduction rates of over 99% can be typically obtained, (ii) multivariate curve resolution - alternating least squares (MCR-ALS) can be easily applied on the reduced data sets and (iii) the full distribution maps and spectral profiles can be readily obtained from the reduced profiles and the reduced data sets (without using the full data matrix). (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A method is proposed for the joint selection of essential samples and variables in a data matrix for spectral unmixing, leading to a highly-reduced dataset with benefits such as minimized computational effort, meticulous data mining, easier model building, and better problem understanding or interpretation. The approach allows for reduction rates of over 99%, easy application of multivariate curve resolution - alternating least squares (MCR-ALS) on reduced data sets, and ready acquisition of full distribution maps and spectral profiles.