@inproceedings {2317,
	title = {An Extensive Multisensor Hyperspectral Benchmark Datasets of Intimate Mixtures of Mineral Powders},
	booktitle = { IEEE International Geoscience and Remote Sensing Symposium},
	year = {2023},
	month = {20 October 2023},
	pages = {5890-5893},
	abstract = {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 hyperspectral dataset of intimate mineral powder mixtures by homogeneously mixing five different clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide). In total 325 samples were prepared. Among the 325 samples, 60 mixtures were binary, 150 were ternary, 100 were quaternary, and 15 were quinary. For each mixture (and pure clay powder), reflectance spectra are acquired by 13 different sensors, with a broad wavelength range between the visible and the long-wavelength infrared regions (i.e., between 350 nm and 15385 nm) and with a large variation in sensor types, platforms, and acquisition conditions. We will make this dataset public, to be used by the community for the validation of nonlinear unmixing methodologies (https://github.com/VisionlabUA/Multisensor_datasets)},
	doi = {10.1109/IGARSS52108.2023.10281467},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@mastersthesis {2339,
	title = {Hyperspectral imaging for automated inspection of offshore wind infrastructure},
	year = {2023},
	type = {PhD thesis},
	author = {Thomas De Kerf}
}
@article {2324,
	title = {A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures},
	journal = {IEEE Sensors Journal},
	year = {2023},
	month = {2023/12/28},
	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. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions 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/VisionlabHyperspectral/Multisensor_datasets.},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Sandra Lorenz and Margret Fuchs and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@article {2208,
	title = {Identification of corrosion minerals usning shortwave infrared hyperspectral imaging},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	pages = {407},
	abstract = {In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900{\textendash}1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time. },
	author = {Thomas De Kerf and G. Pipintakos and Zohreh Zahiri and Steve Vanlanduit and Paul Scheunders}
}
