A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation

TL;DR

This paper introduces a novel, publicly available hyperspectral dataset with ground-truth, created through controlled laboratory experiments, to facilitate the evaluation and comparison of spectral unmixing algorithms.

Contribution

It presents the first systematic laboratory-created hyperspectral dataset with ground-truth for spectral unmixing evaluation, enabling more accurate benchmarking.

Findings

Dataset includes 36 mixtures with over 130,000 pixels across 256 wavelengths.

Experimental settings ensure known pure spectral signatures and compositions.

Testing of typical unmixing algorithms yields meaningful performance insights.

Abstract

Spectral unmixing is an important and challenging problem in hyperspectral data processing. This topic has been extensively studied and a variety of unmixing algorithms have been proposed in the literature. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms in a quantitative and objective manner. Most of the existing works rely on the use of numerical synthetic data and an intuitive inspection of the results of real data. To alleviate this dilemma, in this study, we design several experimental scenes in our laboratory, including printed checkerboards, mixed quartz sands, and reflection with a vertical board. A dataset is then created by imaging these scenes with the hyperspectral camera in our laboratory, providing 36 mixtures with more than 130, 000 pixels with 256 wavelength bands ranging from 400nm to 1000nm. The experimental settings are strictly controlled so that pure material spectral signatures and material compositions are known. To the best of our knowledge, this dataset is the first publicly available dataset created in a systematic manner with ground-truth for spectral unmixing. Some typical linear and nonlinear unmixing algorithms are also tested with this dataset and lead to meaningful results.