A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures

TL;DR

This paper presents a comprehensive hyperspectral dataset of mineral mixtures with ground truth compositions, acquired using multiple sensors across a broad spectral range, to facilitate validation of unmixing algorithms and material analysis.

Contribution

The authors created a large, multisensor hyperspectral dataset with detailed ground truth for intimate mineral mixtures, enabling improved validation of unmixing methods and spectral variability studies.

Findings

Generated 325 mineral mixture samples with known compositions.

Acquired spectra using 13 sensors across 350-15385 nm.

Provided data for nonlinear unmixing validation and supervised learning.

Abstract

Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. {\color{black} Overlaps in wavelength regions due to the operational ranges of each sensor} and variations in acquisition conditions {\color{black} resulted in} a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabUA/Multisensor_datasets.