@inproceedings {1700,
	title = {Compression Of Hyperspectral Images Using Block Coordinate Descent Search And Compressed Sensing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = {Los Angeles, USA},
	author = {Shirin Hassanzadeh and Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1699,
	title = {Denoising Of Hyperspectral Images Using Shearlet Transform And Fully Constrained Least Squares Unmixing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1682,
	title = {Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {9},
	year = {2016},
	pages = {2377-2389},
	abstract = {Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyperspectral images (HSI) is limited, due to sensor limitations. In many applications, HSI with a high spectral as well as spatial resolution are required. In this paper, a new method for spatial resolution enhancement of a HSI using spectral unmixing and sparse coding (SUSC) is introduced. The proposed method fuses high spectral resolution features from the HSI with high spatial resolution features from an MSI of the same scene. Endmembers are extracted from the HSI by spectral unmixing, and the exact location of the endmembers is obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem which requires a regularization term in order to convert it into a well-posed inverse problem. As a regularizer, we employ sparse coding (SC), for which a dictionary is constructed using high spatial resolution MSI or PAN images from unrelated scenes. The proposed algorithm is applied to real Hyperion and ROSIS datasets. Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing, and SC methods, the proposed method is shown to significantly increase the spatial resolution while perserving the spectral content of the HSI.},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2016.2528339},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1681,
	title = {Hyperspectral Image Compression Optimized for Spectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {pp},
	year = {2016},
	month = {06/2016},
	chapter = {1},
	abstract = {In this paper, we present a new lossy compression method for hyperspectral images that aims to optimally compress in both spatial and spectral domains and simultaneously minimizes the effect of the compression on linear spectral unmixing performance. To achieve this, a nonnegative Tucker decomposition is applied. This decomposition is a function of three dimension parameters. By employing a link between this decomposition and the linear spectral mixing model, an optimization problem is defined to find the optimal parameters by minimizing the root-mean-square error between the abundance matrices of the original and reconstructed data sets. The resulting optimization problem is solved by a particle swarm optimization algorithm. An approximate method for fast estimation of the free parameters is introduced as well. Our simulation results show that, in comparison with well-known state-of-the-art lossy compression methods, an improved compression and spectral unmixing performance of the reconstructed hyperspectral image is obtained. It is noteworthy to mention that the superiority of our method becomes more apparent as the compression ratio grows.},
	issn = {0196-2892},
	doi = {10.1109/TGRS.2016.2574757},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1685,
	title = {Hyperspectral unmixing with endmember variability via alternating angle minimization},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {54},
	year = {2016},
	pages = {4983-4993},
	author = {Rob Heylen and Alina Zare and Paul Gader and Paul Scheunders}
}
@article {1640,
	title = {A multilinear mixing model for nonlinear spectral unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {54},
	year = {2016},
	pages = {240-251},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1697,
	title = {Optical Solutions for Improving Spatial Resolution of Hyperspectral Sensors},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Ashkan  Adibi and Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1698,
	title = {Superresolution Of Hyperspectral Images Using Spectral Unmixing And Sparse Regularization},
	booktitle = {IGARSS  2016},
	year = {2016},
	month = {07/2016},
	address = {Beijing China},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1637,
	title = {A fast alternative for the pixel purity index algorithm},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {1781-1784},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {1528,
	title = {A geometric matched filter for hyperspectral target detection and partial unmixing},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {12},
	number = {3},
	year = {2015},
	pages = {661-665},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@article {1526,
	title = {A geometric unmixing concept for the selection of optimal binary endmember combinations},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {12},
	number = {1},
	year = {2015},
	pages = {82-86},
	author = {Laurent Tits and Rob Heylen and Ben Somers and Paul Scheunders and Pol Coppin}
}
@inproceedings {1638,
	title = {Hyperspectral unmixing with projection onto convex sets using distance geometry},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {5059-5062},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@inproceedings {1639,
	title = {Lossy Compression of hyperspectral images optimizing spectral unmixing},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {5031-034},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1529,
	title = {Nonlinear unmixing by using different metrics in a linear unmixing chain},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2015},
	author = {Rob Heylen and Paul Scheunders and Paul Gader and A. Rangarajan}
}
@inproceedings {1636,
	title = {Nonlinear unmixing with a multilinear mixing model},
	booktitle = {IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo},
	year = {2015},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1525,
	title = {A distance geometric framework for non-linear hyperspectral unmixing},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {7},
	number = {6},
	year = {2014},
	pages = {1879-1888},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1531,
	title = {Geometric matched filter for hyperspectral partial unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@inproceedings {1532,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1569,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing},
	year = {2014},
	address = { Lausanne, Suisse},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1534,
	title = {Hyperspectral unmixing using an active set algorithm},
	booktitle = {IEEE ICIP 2014, International Conference on Image Processing, October 27-30, Paris, France },
	year = {2014},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1530,
	title = {Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Rob Heylen and Paul Scheunders and A. Rangarajan and Paul Gader}
}
@article {1527,
	title = {A spectral-unmixing approach to estimate water-mass concentrations in case-II waters},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {7},
	number = {8},
	year = {2014},
	author = {Dzevdet Burazerovic and Rob Heylen and Dries Raymaekers and Els Knaeps and Paul Scheunders}
}
@inproceedings {1523,
	title = {Handling spectral variability with alternating angle minimization},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28},
	year = {2013},
	author = {Rob Heylen and Paul Scheunders and Paul Gader}
}
@inproceedings {1524,
	title = {Improving the efficiency of MESMA through geometric unmixing principles},
	booktitle = {SPIE Remote Sensing, Drseden, Germany, 23-26 September },
	volume = {8892},
	number = {UNSP 88920Q},
	year = {2013},
	author = {Laurent Tits and Ben Somers and Rob Heylen and Paul Scheunders and Pol Coppin}
}
@inproceedings {1522,
	title = {Multi-dimensional Pixel Purity Index},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28},
	year = {2013},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1509,
	title = {Solving the Hyperspectral Unmixing Problem with Projection Onto Convex Sets},
	booktitle = {21st European Signal Processing Conference (EUSIPCO), September 2013, Marrakech, Morocco},
	year = {2013},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {1505,
	title = {On using projection onto convex sets for solving the hyperspectral unmixing problem},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	year = {2013},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
