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Koirala, B.; Rasti, B.; Bnoulkacem, Z.; de Lima Ribeiro, A.; Madriz, Y.; Herrmann, E.; Gestels, A.; De Kerf, T.; Lorenz, S.; Fuchs, M.; Janssens, K.; Steenackers, G.; Gloaguen, R.; Scheunders, P. |
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Title |
A multisensor hyperspectral benchmark dataset for unmixing of intimate mixtures |
Type |
A1 Journal article |
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Year  |
2024 |
Publication |
IEEE sensors journal |
Abbreviated Journal |
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Volume |
24 |
Issue |
4 |
Pages |
4694-4710 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Vision lab; Antwerp X-ray Imaging and Spectroscopy (AXIS) |
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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. |
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Wos |
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Publication Date |
2023-12-28 |
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Series Issue |
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Edition |
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ISSN |
1530-437x; 1558-1748 |
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Additional Links |
UA library record |
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Impact Factor |
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Times cited |
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Open Access |
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Notes |
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Approved |
no |
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Call Number |
UA @ admin @ c:irua:203094 |
Serial |
9059 |
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Author |
Yagmurcukardes, N.; Bayram, A.; Aydin, H.; Yagmurcukardes, M.; Acikbas, Y.; Peeters, F.M.; Celebi, C. |
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Title |
Anisotropic etching of CVD grown graphene for ammonia sensing |
Type |
A1 Journal article |
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Year |
2022 |
Publication |
IEEE sensors journal |
Abbreviated Journal |
Ieee Sens J |
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Volume |
22 |
Issue |
5 |
Pages |
3888-3895 |
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Keywords |
A1 Journal article; Condensed Matter Theory (CMT) |
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Abstract |
Bare chemical vapor deposition (CVD) grown graphene (GRP) was anisotropically etched with various etching parameters. The morphological and structural characterizations were carried out by optical microscopy and the vibrational properties substrates were obtained by Raman spectroscopy. The ammonia adsorption and desorption behavior of graphene-based sensors were recorded via quartz crystal microbalance (QCM) measurements at room temperature. The etched samples for ambient NH3 exhibited nearly 35% improvement and showed high resistance to humidity molecules when compared to bare graphene. Besides exhibiting promising sensitivity to NH3 molecules, the etched graphene-based sensors were less affected by humidity. The experimental results were collaborated by Density Functional Theory (DFT) calculations and it was shown that while water molecules fragmented into H and O, NH3 interacts weakly with EGPR2 sample which reveals the enhanced sensing ability of EGPR2. Apparently, it would be more suitable to use EGRP2 in sensing applications due to its sensitivity to NH3 molecules, its stability, and its resistance to H2O molecules in humid ambient. |
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Wos |
000766276000010 |
Publication Date |
2022-01-24 |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1530-437x; 1558-1748 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
4.3 |
Times cited |
2 |
Open Access |
Not_Open_Access |
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Notes |
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Approved |
Most recent IF: 4.3 |
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Call Number |
UA @ admin @ c:irua:187257 |
Serial |
7126 |
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