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

arXiv: https://arxiv.org/abs/1902.08347 · HTML: https://mlsanity.com/paper/1902.08347/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors](https://mlsanity.com/paper/2211.15307/) (2022)
- [UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields](https://mlsanity.com/paper/2506.21884/) (2025)
- [A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures](https://mlsanity.com/paper/2309.03216/) (2023)
- [Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data](https://mlsanity.com/paper/2411.10924/) (2024)
- [Robust blind unmixing: A geometric approach to overcoming basis variation](https://mlsanity.com/paper/2610.04091/) (2026)

## Abstract (arXiv)

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.