Publications

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Journal Article
B. Koirala, Zahiri, Z., and Scheunders, P., “A Machine Learning Framework for Estimating Leaf Biochemical Parameters From Its Spectral Reflectance and Transmission Measurements”, IEEE Transactions on Geoscience and Remote Sensing, vol. 99, pp. 1-13, 2020.
P. Scheunders, “Local mapping for multispectral image visualisation”, Image and Vision Computing, vol. 19, pp. 971-978, 2001.
P. Scheunders, “Joint quantization and error-diffusion of color images using competitive learning”, Journal of the IEE Proceedings, Vision, Image and Signal Processing, vol. 14, pp. 137-140, 1998.
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.
J. Bioucas-Dias, Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., and Chanussot, J., “Hyperspectral remote sensing data analysis and future challenges”, IEEE Geoscience and Remote Sensing Magazine, vol. 1, no. 2, pp. 6-36, 2013.
M. Brackx, Van Wittenberghe, S., Verhelst, J., Scheunders, P., and Samson, R., “Hyperspectral leaf reflectance of Carpines betulus L. saplings for urban air quality estimation”, Environmental Pollution, vol. 220, pp. 159-167, 2017.
R. Heylen and Scheunders, P., “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratio's”, IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 570-579, 2013.
T. Hu, Li, W., Liu, N., Tao, R., Zhang, F., and Scheunders, P., “Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms”, IEEE Transactions on Geoscience and Remote Sensing, pp. 1-18, 2020.
A. Karami, Heylen, R., and Scheunders, P., “Hyperspectral Image Compression Optimized for Spectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. pp, no. 99, 2016.
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, 2020.
P. Scheunders and De Backer, S., “High-dimensional clustering using frequency sensitive competitive learning”, Pattern Recognition, vol. 32, pp. 193-202, 1999.
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.
B. Haest, Vanden Borre, J., Spanhove, T., Thoonen, G., Delalieux, S., Kooistra, L., Mücher, C. A., Paelinckx, D., Scheunders, P., and Kempeneers, P., “Habitat mapping and quality assessment of NATURA 2000 Heatland using airborne imaging spectroscopy”, Remote Sensing, vol. 9, no. 3, 2017.
G. Verdoolaege and Scheunders, P., “On the geometry of Multivariate Generalized Gaussian models”, Journal of Mathematical Imaging and Vision, vol. 43, no. 3, pp. 180-193, 2012.
L. Tits, Heylen, R., Somers, B., Scheunders, P., and Coppin, P., “A geometric unmixing concept for the selection of optimal binary endmember combinations”, IEEE Geoscience and Remote Sensing letters, vol. 12, pp. 82-86, 2015.
M. A. Akhter, Heylen, R., and Scheunders, P., “A geometric matched filter for hyperspectral target detection and partial unmixing”, IEEE Geoscience and Remote Sensing letters, vol. 12, pp. 661-665, 2015.
G. Verdoolaege and Scheunders, P., “Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions With an Application to Multicomponent Texture Discrimination”, International Journal of Computer Vision, vol. 95, pp. 265-286, 2011.
P. Scheunders, “A genetic Lloyd-Max image quantization algorithm”, Pattern Recognition Letters, vol. 17, pp. 547-556, 1996.
S. Yu, De Backer, S., and Scheunders, P., “Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery”, Pattern Recognition Letters, vol. 23, pp. 183-190, 2002.
P. Scheunders, “A genetic c-means clustering algorithm applied to color image quantization”, Pattern Recognition, vol. 30, pp. 859-866, 1997.
P. Kempeneers, De Backer, S., Debruyn, W., and Scheunders, P., “Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection”, IEEE Transactions on Geoscience and Remote Sensing, vol. 43, pp. 610-614, 2005.
Z. H. Nezhad, Karami, A., Heylen, R., and Scheunders, P., “Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2377-2389, 2016.
R. Luo, Liao, W., Zhang, H., Zhang, L., Pi, Y., Scheunders, P., and Philips, W., “Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensing Scene”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 8, pp. 3768-3781, 2017.
P. Scheunders and De Backer, S., “Fusion and merging of multispectral images using multiscale fundamental forms”, Journal of the Optical Society of America A, vol. 18, pp. 2468-2477, 2001.
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)

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