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Journal Article
S. De Backer, Naud, A., and Scheunders, P., Non-linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction, Pattern Recognition Letters, vol. 19, pp. 711-720, 1998.
R. Heylen, Andrejchenko, V., Zahiri, Z., Parente, M., and Scheunders, P., Nonlinear hyperspectral unmixing with graphical models, IEEE Transaction on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4844-4856, 2019.PDF icon published.pdf (3.15 MB)
P. Scheunders, De Backer, S., and Naud, A., Non-linear mapping for feature extraction, Lecture Notes in Computer Science, vol. 1451, pp. 823-830, 1998.
R. Heylen, Burazerovic, D., and Scheunders, P., Nonlinear spectral unmixing by geodesic simplex volume maximization, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 534-542, 2011.
R. Heylen, Scheunders, P., Gader, P., and Rangarajan, A., Nonlinear unmixing by using different metrics in a linear unmixing chain, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015.
M. Baillieux and Scheunders, P., On-line determination of the velocity of simultaneously moving organisms by image analysis for the detection of sublethal toxicity, Water Research, vol. 32, pp. 1027-1034, 1998.
P. Scheunders, An orthogonal wavelet representation of multivalued images, IEEE Transactions on Image Processing, vol. 12, pp. 718-725, 2003.
J. Sijbers, Scheunders, P., Bonnet, N., Van Dyck, D., and Raman, E., Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure, Magnetic Resonance Imaging, vol. 14, pp. 1157-1163, 1996.PDF icon Download paper (1.31 MB)
B. Koirala, Zahiri, Z., Lamberti, A., and Scheunders, P., Robust supervised method for nonlinear spectral unmixing accounting for endmember variability, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7434-7448, 2021.PDF icon ieee_version.pdf (3.76 MB)
W. Liao, Pizurica, A., Scheunders, P., Philips, W., and Pi, Y., Semi-supervised local discriminant analysis for feature extraction in hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 184-198, 2013.
G. Liu, Nath, T., Guo, Z., Linneweber, G., Claeys, A., Li, J., Bengochea, M., De Backer, S., Weyn, B., Sneyders, M., Nicasy, H., Yu, P., Scheunders, P., and Hassan, B., A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila, Plos Computational Biology, vol. 14, no. 8, p. e1006410, 2018.
E. K. Ghasrodashti, Karami, A., Heylen, R., and Scheunders, P., Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation, Remote Sensing, vol. 9, no. 6, 2017.
V. Van Meir, Boumans, T., De Groof, G., Van Audekerke, J., Smolders, A., Scheunders, P., Sijbers, J., Verhoye, M., Balthazart, J., and Van Der Linden, A., Spatiotemporal properties of the BOLD response in the songbirds auditory circuit during a variety of listening tasks, NeuroImage, vol. 25, pp. 1242-1255, 2005.
D. Burazerovic, Heylen, R., Raymaekers, D., Knaeps, E., and Scheunders, P., A spectral-unmixing approach to estimate water-mass concentrations in case-II waters, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, 2014.
G. Van de Wouwer, Scheunders, P., and Van Dyck, D., Statistical texture characterization from discrete wavelet representations, IEEE Transactions on Image Processing, vol. 8, pp. 592-598, 1999.
B. Koirala, Khodadadzadeh, M., Contreras, C., Zahiri, Z., Gloaguen, R., and Scheunders, P., A supervised method for nonlinear hyperspectral unmixing, Remote Sensing, vol. 11, no. 20 , 2019.PDF icon remotesensing-11-02458-v3.pdf (3.23 MB)
S. Livens, Scheunders, P., Van de Wouwer, G., Van Dyck, D., Smets, H., Winkelmans, J., and Bogaerts, W., A texture analysis approach to corrosion image classification, Microscopy, Microanalysis, Microstructures, vol. 7, pp. 1-10, 1996.
S. De Backer and Scheunders, P., Texture segmentation by frequency-sensitive elliptical competitive learning, Image and Vision Computing, vol. 19, pp. 639-648, 2001.
W. Van den Broek, Verbeeck, J., Schryvers, D., De Backer, S., and Scheunders, P., Tomographic Spectroscopic Imaging; an experimental proof of concept, Ultramicroscopy, vol. 109, pp. 296-303, 2009.
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., UnDIP: hyperspectral unmixing using deep image prior, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.PDF icon manuscript.pdf (13 MB)
K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Scheunders, P., and Gloaguen, R., Unsupervised Data Fusion with Deeper Perspective: A Novel Multi-Sensor Deep Clustering Algorithm, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15, pp. 284-296, 2022.PDF icon mdc_jstars-final_version.pdf (6.53 MB)
M. Brackx, Verhelst, J., Scheunders, P., and Samson, R., On the use of dorsiventral reflectance asymmetry of hornbeam (Carpinus betulus L.) leaves in air pollution estimation, Environmental Monitoring and Assessment, vol. 189, no. 9, 2017.
C. C. Hung, Scheunders, P., Pham, M., Su, M. C., and Coleman, T., Using Intelligent Optimization Techniques in the K-means Algorithm for Multispectral Image Classification, International Journal of Fuzzy Systems, vol. 6, pp. 107-117, 2004.
R. Heylen, Akhter, M. A., and Scheunders, P., On using projection onto convex sets for solving the hyperspectral unmixing problem, IEEE Geoscience and Remote Sensing Letters, 2013.
B. Weyn, Tjalam, W., Van de Wouwer, G., Van Daele, A., Scheunders, P., Jacob, W., Van Marck, E., and Van Dyck, D., Validation of nuclear texture density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma, Analytical and Quantitative Cytology and Histology, vol. 22, pp. 373-382, 2000.