Extending the Unmixing methods to Multispectral Images

TL;DR

This paper extends hyperspectral unmixing methods like VCA, NMF, and N-FINDR to multispectral images, demonstrating their potential and analyzing their effectiveness through simulated datasets.

Contribution

The paper adapts and evaluates existing hyperspectral unmixing algorithms for multispectral images, a less-explored area, showing their applicability and performance.

Findings

VCA, NMF, and N-FINDR can be applied to multispectral data

Simulated multispectral datasets enable comparison of unmixing methods

Results suggest potential for extending hyperspectral unmixing techniques

Abstract

In the past few decades, there has been intensive research concerning the Unmixing of hyperspectral images. Some methods such as NMF, VCA, and N-FINDR have become standards since they show robustness in dealing with the unmixing of hyperspectral images. However, the research concerning the unmixing of multispectral images is relatively scarce. Thus, we extend some unmixing methods to the multispectral images. In this paper, we have created two simulated multispectral datasets from two hyperspectral datasets whose ground truths are given. Then we apply the unmixing methods (VCA, NMF, N-FINDR) to these two datasets. By comparing and analyzing the results, we have been able to demonstrate some interesting results for the utilization of VCA, NMF, and N-FINDR with multispectral datasets. Besides, this also demonstrates the possibilities in extending these unmixing methods to the field of multispectral imaging.