Publications

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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.PDF icon ieee_journal_misicnet.pdf (11.02 MB)
B. Rasti, Koirala, B., and Scheunders, P., “Sparse Unmixing using Deep Convolutional Networks”, in IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 24-27.PDF icon suncnn_igarss2022.pdf (1.03 MB)
B. Rasti, Koirala, B., and Scheunders, P., “Sparse unmixing using deep convolutional networks”, in IGARSS 2022, International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.
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.PDF icon nonlinear_unmixing.pdf (8.14 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “Spectral Unmixing Using Deep Convolutional Encoder-Decoder”, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3829-3832.PDF icon igarss2021.pdf (659.27 KB)
B. Rasti and Koirala, B., “SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network”, IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.PDF icon ieee_grsl_sundip.pdf (2.34 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, no. 1728, 2020.PDF icon remotesensing-12-01728.pdf (2.27 MB)
B. Rasti, Koirala, B., Scheunders, P., Ghamisi, P., and Gloaguen, R., “Boosting Hyperspectral Image Unmixing using Denoising: Four Scenarios”, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.
B. Rasti, Koirala, B., Scheunders, P., Ghamisi, P., and Gloaguen, R., “BOOSTING HYPERSPECTRAL IMAGE UNMIXING USING DENOISING: FOUR SCENARIOS”, in IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021.
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, 2021.PDF icon manuscript.pdf (13 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “Spectral Unmixing Using Deep Convolutional Encoder-Decoder”, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “Deep Blind Unmixing using Minimum Simplex Convolutional Network”, in IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 28-31.PDF icon misicnet_igarss2022.pdf (1.29 MB)
B. Rasti, Scheunders, P., Ghesami, P., Licciardi, G., and Chanussot, J., “Noise reduction in hyperspectral imagery: overview and application”, Remote Sensing , vol. 10, no. 3, p. 482, 2018.
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, 2022.PDF icon hapke_cnn.pdf (8.14 MB)
B. Rasti, 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, no. 5522815, 2022.PDF icon misicnet_ieee_tgrs_author_version.pdf (5.57 MB)
B. Rasti and Koirala, B., “Blind Nonlinear Unmixing For Intimate Mixtures Using Hapke Model And CNN”, in Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2022, pp. 1-5.PDF icon whispers_2022_hapkecnn.pdf (1.93 MB)
A. J. Rebelo, Scheunders, P., Esler, K. J., and Meire, P., “Evaluating palmiet wetland decline: a comparison of three methods”, Remote Sensing Applications: Society and Environment, vol. 8, pp. 212-223, 2017.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Constrained spherical deconvolution based tractography and cognition in Alzheimer’s disease”, Human Brain Mapping. Québec, Canada, 2011.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Improved sensitivity to cerebral white matter abnormalities in Alzheimer's disease with spherical deconvolution based tractography.”, PloS one, vol. 7, no. 8, p. e44074, 2012.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Constrained spherical deconvolution based tractography and cognition in Alzheimer’s disease”, Congress of the International Society for Vascular, Cognitive and Behavioural Disorders, vol. 5. Lille, France, 2011.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Constrained spherical deconvolution based tractography and cognition in Alzheimer’s disease”, International Conference on Alzheimer’s Disease. Paris, France, 2011.
P. Reischig, King, A., Nervo, L., Viganó, N., Guilhem, Y., Palenstijn, W. J., Batenburg, K. J., Preuss, M., and Ludwig, W., “Advances in X-ray diffraction contrast tomography: flexibility in the setup geometry and application to multiphase materials”, Journal of Applied Crystallography, vol. 46, no. 2, pp. 297 - 311, 2013.
J. Renders, Nguyen, A. - T., De Beenhouwer, J., and Sijbers, J., “Motion compensating X-ray micro-CT of diamonds in a processing stage”, 31st International Conference on Diamond and Carbon Materials. 2021.PDF icon Download poster (1.04 MB)
J. Renders, De Beenhouwer, J., and Sijbers, J., “Mesh-based reconstruction of dynamic foam images using X-ray CT”, in International Conference on 3D Vision (3DV2021), 2021, pp. 1312-1320.
J. Renders, “Improved 4DCT reconstruction algorithms for the imaging of foam microstructure formation”, 2024.

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