Paul Scheunders

Personal

Biography: 

Paul Scheunders graduated in physics at the University of Antwerp in 1986. He obtained a PhD in statistical physics at the same university in 1990. From 1989 to 1990, he worked in the Physics Department at the University of Louvain. From 1990 to 1992, he worked in the Nuclear Medicine division of the University Hospital of Louvain. In 1992 he and Prof. Dirk Van Dyck started the Vision Lab at the University of Antwerp, where he is currently a Professor.

Research

My general research interest is in the development of techniques for image processing and machine learning with a strong emphasis on model-based and statistical techniques. My main application area is remote sensing in general and multispectral and hyperspectral image processing and analysis in particular. Recent work shifted towards close-range applications, for which we use our own acquisition equipment: a spectrometer (400-2500 nm), a VNIR hyperspectral camera (400-900 nm) and a SWIR hyperspectral camera (1000-1700 nm).
Some specific research topics are:

• Development of lineair and non-linear spectral unmixing methods
• Material characterization: material composition, water content estimation, biophysical parameter estimation
• Applications related to clay and powder composition estimation
• Applications related to moisture content of soil and construction materials
• Applications related to corrosion monitoring

Key publications: 

B. Koirala, Rasti, B., Bnoulkacem, Z., Ribeiro, A. De Lima, Madriz, Y., Herrmann, E., Gestels, A., De Kerf, T., Lorenz, S., Fuchs, M., Janssens, K., Steenackers, G., Gloaguen, R., and Scheunders, P., “A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures”, IEEE Sensors Journal, 2023.

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a_multisensor_hyperspectral_benchmark_dataset_for_unmixing_of_intimate_mixtures_1_.pdf (22.13 MB)

B. Koirala, Zahiri, Z., and Scheunders, P., “A Robust Supervised Method for Estimating Soil Moisture Content From Spectral Reflectance”, IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1-13, 2022.

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soil_moisture_content.pdf (7.95 MB)

B. Rasti, Koirala, B., and Scheunders, P., “HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.

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nonlinear_unmixing.pdf (8.14 MB)

P. Ghosh, Roy, S. Kumar, Koirala, B., Rasti, B., and Scheunders, P., “Hyperspectral Unmixing Using Transformer Network”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022.

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transformer_unmixing.pdf (6.32 MB)

B. Rast, Koirala, B., Scheunders, P., and Chanussot, J., “MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.

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ieee_journal_misicnet.pdf (11.02 MB)

Teaching

Teaching activities: 

• Mathematical Methods for Physics II; 1-st Bachelor Physics
• Introduction Classic Field Theory; 2-th Bachelor Physics
• Theoretical Physics: Classic Field Theory; 3-th Bachelor Physics
• Artificial Neural Networks; Master Physics