@article {2204,
	title = {3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts},
	journal = {Measurement Science and Technology},
	volume = {33},
	year = {2022},
	pages = {1-12},
	doi = {10.1088/1361-6501/ac459a},
	author = {Rob Heylen and Aditi Thanki and Dries Verhees and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers and Ann Witvrouw and Han Haitjema and Abdellatif Bey-Temsamani}
}
@inproceedings {2243,
	title = {Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122421K},
	doi = {https://doi.org/10.1117/12.2633318},
	author = {Domenico Iuso and Soumick Chatterjee and Rob Heylen and Sven Cornelissen and Jan De Beenhouwer and Jan Sijbers}
}
@article {1913,
	title = {Nonlinear hyperspectral unmixing with graphical models},
	journal = {IEEE Transaction on Geoscience and Remote Sensing},
	volume = {57},
	year = {2019},
	pages = {4844-4856},
	author = {Rob Heylen and Vera Andrejchenko and Zohreh Zahiri and Mario Parente and Paul Scheunders}
}
@inproceedings {2008,
	title = {A spectral mixing model accounting for multiple reflections and shadow},
	booktitle = {IGARSS 2019, International Geoscience and Remote Sensing Symposium},
	year = {2019},
	pages = {286-289},
	address = {Yokohama, Japan},
	doi = {10.1109/IGARSS.2019.8897856},
	author = {Vera Andrejchenko and Zohreh Zahiri and Rob Heylen and Paul Scheunders}
}
@inproceedings {1870,
	title = {MRF-based decision fusion for hyperspectral image classification},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	address = {Valencia, Spain},
	abstract = {The high dimensionality of hyperspectral images, the limited availability of ground-truth data as well as the low spatial resolution (causing pixels to contain mixtures of materials) hinder hyperspectral image classification. In this work we propose a novel hyperspectral classification method where we combine the outcome of spectral unmixing with the outcome
of a supervised classifier. In particular, we consider fractional abundances obtained from a Sparse Unmixing method along
with posterior probabilities acquired from a Multinomial Logistic Regression classifier. Both sources of information are
fused using a Markov Random Field framework. We conducted experiments on publicly available real hyperspectral
images: Indian Pines and University of Pavia using a very limited number of training samples. Our results indicate that
the proposed decision fusion approach significantly improves the classification result over using the individual sources and outperforms the state of the art methods.},
	url = {https://www.igarss2018.org/Papers/viewpapers.asp?papernum=2751},
	author = {Vera Andrejchenko and Rob Heylen and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@inproceedings {1875,
	title = {A NEURAL NETWORK METHOD FOR NONLINEAR HYPERSPECTRAL UNMIXING},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	pages = {pp. 4233-4236},
	address = {Valencia, Spain},
	abstract = {Because of the complex interaction of light with the Earth surface, a hyperspectral pixel can be composed of a highly nonlinear mixture of the reflectances of the materials on the ground. When nonlinear mixing models are applied, the estimated model parameters are usually hard to interpret and to link to the actual fractional abundances. Moreover, not all spectral reflectances in a real scene follow the same particular mixing model. In this paper, we present a supervised learning method for nonlinear spectral unmixing. In this method, a neural network is applied to learn mappings of the true spectral reflectances to the reflectances that would be obtained if the mixture was linear. A simple linear unmixing then reveals the actual abundance fractions. This technique is model-independent and allows for an easy interpretation of the obtained abundance fractions. We validate this method on several artificial datasets, a data set obtained by ray tracing, and a real dataset.},
	doi = {doi: 10.1109/IGARSS.2018.8518995},
	url = {https://ieeexplore.ieee.org/document/8518995},
	author = {Bikram Koirala and Rob Heylen and Paul Scheunders}
}
@inproceedings {1897,
	title = {Spectral variability in a multilinear mixing model},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {T Dox and Rob Heylen and Paul Scheunders}
}
@inproceedings {1807,
	title = {Estimation of the intrinsic dimensionality in hyperspectral imagery via the hubness phenomenon},
	booktitle = {LVA ICA 2017, International conference on latent variable analysis and signal separation, Grenoble, France, February 21-23, Lecture Notes in Computer Science},
	volume = {10169},
	year = {2017},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1800,
	title = {Estimation of the number of endmembers in a hyperspectral image via the hubness phenomenon},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {55},
	year = {2017},
	pages = {2191-2200},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@inproceedings {1808,
	title = {Pixel purity vertex component analysis},
	booktitle = {IEEE IGARSS 2017, International Geoscience and Remote Sensing Symposium, Fort Worth, USA, July 23-28},
	year = {2017},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1804,
	title = {Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {541},
	author = {E.K. Ghasrodashti and Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1809,
	title = {Alternating angle minimization based unmixing with endmember variability},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 6974-6977, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7730819},
	author = {Rob Heylen and Paul Scheunders and Alina Zare and Paul Gader}
}
@inproceedings {1717,
	title = {Classification of hyperspectral images with very small training size using sparse unmixing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2016},
	address = {Beijing, China},
	author = {Vera Andrejchenko and Rob Heylen and Paul Scheunders and Wilfried Philips and Wenzhi Liao}
}
@article {1571,
	title = {Band-specific Shearlet-based Hyperspectral Image Noise Reduction},
	journal = {IEEE Transaction Geosciences and Remote Sensing },
	volume = {53},
	number = {13},
	year = {2015},
	month = {03/2015},
	chapter = {1},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1409,
	title = {Detecting the adjacency effect in hyperspectral imagery with spectral unmixing techniques},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	pages = {1070-1078},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@article {1408,
	title = {Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratio{\textquoteright}s},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	pages = {570-579},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1371,
	title = {Multi-dimensional pixel purity index for convex hull estimation and endmember extraction},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {51},
	year = {2013},
	pages = {4059-4069},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1316,
	title = {Calculation of geodesic distances in non-linear mixing models: demonstration on the generalized bilinear model},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {9},
	year = {2012},
	pages = {644-648},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1351,
	title = {Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {1377-1380},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1354,
	title = {A fast geometric algorithm for solving the inversion problem in spectral unmixing},
	booktitle = {IEEE-WHISPERS 2012, Workshop on Hyperspectral Image and Signal Processing, Shanghai, June 4-7},
	year = {2012},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1353,
	title = {Unmixing for detection and quantification of adjacency effects},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {3090-3093},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@article {HeylenBurazerovicpscheund2011,
	title = {Fully constrained least-squares spectral unmixing by simplex projection},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {49},
	year = {2011},
	pages = {4112-4122},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@inproceedings {Heylenpscheund2011,
	title = {Non-linear fully-constrained spectral unmixing},
	booktitle = {IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium, Vancouver, July 25-29},
	year = {2011},
	author = {Rob Heylen and Paul Scheunders}
}
@article {HeylenBurazerovicpscheund2011,
	title = {Nonlinear spectral unmixing by geodesic simplex volume maximization},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {5},
	number = {3},
	year = {2011},
	pages = {534-542},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@inproceedings {Heylenpscheund2011,
	title = {Spectral unmixing using distance geometry},
	booktitle = {IEEE-WHISPERS 2011, Workshop on Hperspectral Image and Signal Processing, Lisbon, Portugal, 6-9 June},
	year = {2011},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {BurazerovicHeylenpscheund2011,
	title = {Towards streaming hyperspectral endmember extraction},
	booktitle = {IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium , Vancouver, July 25-29},
	year = {2011},
	author = {Dzevdet Burazerovic and Rob Heylen and Paul Scheunders}
}
@inproceedings {HeylenBurazerovicScheunders2010,
	title = {A graph-based method for non-linear unmixing of hyperspectral imagery},
	booktitle = {IEEE IGARSS2010, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, Haway, July 25-30},
	year = {2010},
	pages = {197-200},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@inproceedings {Heylenpscheund2010,
	title = {Nonlinear barycentric dimensionality reduction},
	booktitle = {IEEE ICIP10, IEEE International Conference on Image Processing, Hong Kong, september 26-29},
	year = {2010},
	pages = {1341-1344},
	author = {Rob Heylen and Paul Scheunders}
}
