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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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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 |
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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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no |
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Call Number |
UA @ admin @ c:irua:203094 |
Serial |
9059 |
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Author |
Koirala, B.; Rasti, B.; Bnoulkacem, Z.; De Lima Ribeiro, A.; Madriz, Y.; Herrmann, E.; Gestels, A.; De Kerf, T.; Janssens, K.; Steenackers, G.; Gloaguen, R.; Scheunders, P. |
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Title |
An extensive multisensor hyperspectral benchmark datasets of intimate mixtures of mineral powders |
Type |
P1 Proceeding |
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Year |
2023 |
Publication |
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Abbreviated Journal |
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Volume |
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Issue |
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Pages |
5890-5893
T2 - IGARSS 2023 - 2023 IEEE Internation |
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Keywords |
P1 Proceeding; Vision lab; Antwerp X-ray Imaging and Spectroscopy (AXIS) |
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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) |
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Place of Publication |
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Wos |
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Publication Date |
2023-10-20 |
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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 |
979-83-503-2010-7 |
ISBN |
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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:201596 |
Serial |
9035 |
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Author |
De Kerf, T.; Gestels, A.; Janssens, K.; Scheunders, P.; Steenackers, G.; Vanlanduit, S. |
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Title |
Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging |
Type |
A1 Journal article |
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Year |
2022 |
Publication |
RSC advances |
Abbreviated Journal |
Rsc Adv |
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Volume |
12 |
Issue |
50 |
Pages |
32775-32783 |
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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 |
This study presents a novel method for the detection and quantification of atmospheric corrosion products on carbon steel. Using hyperspectral imaging (HSI) in the short-wave infrared range (SWIR) (900-1700 nm), we are able to identify the most common corrosion minerals such as: alpha-FeO(OH) (goethite), gamma-FeO(OH) (lepidocrocite), and gamma-Fe2O3 (maghemite). Six carbon steel samples were artificially corroded in a salt spray chamber, each sample with a different duration (between 1 h and 120 hours). These samples were analysed by scanning X-ray diffraction (XRD) and also using a SWIR HSI system. The XRD data is used as baseline data. A random forest regression algorithm is used for training on the combined XRD and HSI data set. Using the trained model, we can predict the abundance map based on the HSI images alone. Several image correlation metrics are used to assess the similarity between the original XRD images and the HSI images. The overall abundance is also calculated and compared for XRD and HSI images. The analysis results show that we are able to obtain visually similar images, with error rates ranging from 3.27 to 13.37%. This suggests that hyperspectral imaging could be a viable tool for the study of corrosion minerals. |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Wos |
000885554600001 |
Publication Date |
2022-11-15 |
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Series Editor |
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Series Title |
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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 |
2046-2069 |
ISBN |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
3.9 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
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Approved |
Most recent IF: 3.9 |
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Call Number |
UA @ admin @ c:irua:192085 |
Serial |
7334 |
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Author |
van den Broek, W.; Verbeeck, J.; Schryvers, D.; de Backer, S.; Scheunders, P. |
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Title |
Tomographic spectroscopic imaging; an experimental proof of concept |
Type |
A1 Journal article |
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Year |
2009 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
109 |
Issue |
4 |
Pages |
296-303 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
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Abstract |
Recording the electron energy loss spectroscopy data cube with a series of energy filtered images is a dose inefficient process because the energy slit blocks most of the electrons. When recording the data cube by scanning an electron probe over the sample, perfect dose efficiency is attained; but due to the low current in nanoprobes, this often is slower, with a smaller field of view. In W. Van den Broek et al. [Ultramicroscopy, 106 (2006) 269], we proposed a new method to record the data cube, which is more dose efficient than an energy filtered series. It produces a set of projections of the data cube and then tomographically reconstructs it. In this article, we demonstrate these projections in practice, we present a simple geometrical model that allows for quantification of the projection angles and we present the first successful experimental reconstruction, all on a standard post-column instrument. |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
Amsterdam |
Editor |
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Language |
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Wos |
000265345400003 |
Publication Date |
2008-12-10 |
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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 |
0304-3991; |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.843 |
Times cited |
1 |
Open Access |
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Notes |
Esteem 026019 |
Approved |
Most recent IF: 2.843; 2009 IF: 2.067 |
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Call Number |
UA @ lucian @ c:irua:77271 |
Serial |
3671 |
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| Permanent link to this record |
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Author |
van den Broek, W.; Verbeeck, J.; de Backer, S.; Scheunders, P.; Schryvers, D. |
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Title |
Acquisition of the EELS data cube by tomographic reconstruction |
Type |
A1 Journal article |
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Year |
2006 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
106 |
Issue |
4/5 |
Pages |
269-276 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
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Abstract |
Energy filtered TEM, EFTEM, provides three-dimensional data, two spatial and one spectral dimension. We propose to acquire these data by measuring a series of images with a defocused energy filter. It will be shown that each image is a projection of the data on the detector and that reconstruction of the data out of a sufficient number of such projections using a tomographic reconstruction algorithm is possible. This technique uses only a fraction of the electron dose an energy filtered series (EFS) needs for the same spectral and spatial resolution and the same mean signal-to-noise ratio. (c) 2005 Elsevier B.V. All rights reserved. |
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Place of Publication |
Amsterdam |
Editor |
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Language |
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Wos |
000236042300003 |
Publication Date |
2005-11-30 |
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Series Editor |
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Series Title |
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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 |
0304-3991; |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.843 |
Times cited |
6 |
Open Access |
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Notes |
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Approved |
Most recent IF: 2.843; 2006 IF: 1.706 |
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Call Number |
UA @ lucian @ c:irua:56910UA @ admin @ c:irua:56910 |
Serial |
55 |
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