Publications

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R. Heylen, Andrejchenko, V., Zahiri, Z., Parente, M., and Scheunders, P., Nonlinear hyperspectral unmixing with graphical models, IEEE Transaction on Geoscience and Remote Sensing, 2019.
R. Heylen and Scheunders, P., Non-linear fully-constrained spectral unmixing, in IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium, Vancouver, July 25-29, 2011.
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.
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.
R. Heylen and Scheunders, P., Nonlinear unmixing with a multilinear mixing model, in IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo, 2015.
R. Heylen and Scheunders, P., Multi-dimensional pixel purity index for convex hull estimation and endmember extraction, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 7, pp. 4059-4069, 2013.
R. Heylen, Zare, A., Gader, P., and Scheunders, P., Hyperspectral unmixing with endmember variability via alternating angle minimization, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4983-4993, 2016.
R. Heylen and Scheunders, P., Hyperspectral unmixing using an active set algorithm, in IEEE ICIP 2014, International Conference on Image Processing, October 27-30, Paris, France , 2014.
R. Heylen and Scheunders, P., A multilinear mixing model for nonlinear spectral unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 240-251, 2016.
R. Heylen and Scheunders, P., Spectral unmixing using distance geometry, in IEEE-WHISPERS 2011, Workshop on Hperspectral Image and Signal Processing, Lisbon, Portugal, 6-9 June, 2011.
R. Heylen, Akhter, M. A., and Scheunders, P., Solving the Hyperspectral Unmixing Problem with Projection Onto Convex Sets, in 21st European Signal Processing Conference (EUSIPCO), September 2013, Marrakech, Morocco, 2013.
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 and Scheunders, P., Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances, in IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27, 2012, pp. 1377-1380.
R. Heylen, Scheunders, P., and Gader, P., Handling spectral variability with alternating angle minimization, in IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013.
R. Heylen, Parente, M., and Scheunders, P., Estimation of the number of endmembers in a hyperspectral image via the hubness phenomenon, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 4, pp. 2191-2200, 2017.
R. Heylen, Burazerovic, D., and Scheunders, P., Fully constrained least-squares spectral unmixing by simplex projection, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4112-4122, 2011.PDF icon PDF (1.11 MB)Package icon Matlab code (1.93 KB)
R. Heylen, Scheunders, P., Zare, A., and Gader, P., Alternating angle minimization based unmixing with endmember variability, in IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 6974-6977, Beijing, July 10-15 , 2016.
K. Hufkens, Thoonen, G., Vanden Borre, J., Scheunders, P., and Ceulemans, R., Habitat reporting of a heathland site: Classification probabilities as additional information, a case study, Ecological Informatics, vol. 5, pp. 248 - 255, 2010.
K. Hufkens, Scheunders, P., and Ceulemans, R., Validation of the sigmoid wave curve fitting algorithm on a forest-tundra ecotone in the Northwest Territories, Canada, Ecological Informatics, vol. 4, pp. 1-7, 2009.
K. Hufkens, Ceulemans, R., and Scheunders, P., Estimating the ecotone width in patchy ecotones using a sigmoid wave approach, Ecological Informatics, vol. 3, pp. 97-104, 2008.
K. Hufkens, Ceulemans, R., and Scheunders, P., Ecotones in vegetation ecology: methodology and definitions revisited, Ecological Research, vol. 24, pp. 977-986, 2009.
S. Huijs, Huysmans, T., De Jong, A., Arnout, N., Sijbers, J., and Bellemans, J., Principal component analysis as a tool for determining optimal tibial baseplate geometry in modern TKA design, Acta Orthop Belg, vol. 84, no. 4, pp. 452-460, 2018.
C. C. Hung, Coleman, T., and Scheunders, P., Using genetic differential competitive learning for unsupervised training in multispectral image classification systems, in Proceedings IEEE International Conference on Systems, Man, and Cybernetics , San Diego, California, October 11-14, 1998, pp. 4482-4485.
C. C. Hung, Coleman, T., and Scheunders, P., The genetic algorithm approach and K-means clustering: their role in unsupervised training in image classification, in Proc. IASTED International Conf. On Computer Graphics and Imaging , Halifax, Canada, june 1-3, 1998, pp. 103-106.
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.

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