@inproceedings {1895,
	title = {Fusion of hyperspectral and Lidar images using non-subsampled shearlet transform},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {M. Reza Soleimanzadeh and Azam Karami and Paul Scheunders}
}
@inproceedings {1896,
	title = {Hyperspectral and multispectral image fusion based on spectral matching in the shearlet domain},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {H. Rezaei and Azam Karami 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 {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}
}
@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}
}
@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}
}
@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}
}
@inproceedings {1622,
	title = {Lossy Compression of Hyperspectral Images Optimizing Spectral Unmixing},
	booktitle = {IGARSS  2015},
	year = {2015},
	address = {Milan, Italy},
	author = {Azam Karami and R.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 {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}
}
@article {1459,
	title = {Compression and Noise Reduction of Hyperspectral Images using Tucker Decomposition and Discrete Wavelet Transform},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {5},
	year = {2012},
	chapter = {444},
	author = {Azam Karami and M.Yazdi and G. Mercier}
}
@proceedings {1463,
	title = {Hyperspectral Image Compression Using 3d Discrete Cosine Transform and Support Vector Machine Learning},
	year = {2012},
	address = {Montreal, Canada},
	author = {Azam Karami and S. Beheshti and M.Yazdi}
}
@proceedings {1464,
	title = {Hyperspectral Image Compression based on Tucker Decomposition and Wavelet Transform},
	year = {2011},
	address = {Lisbon, Portugal},
	author = {Azam Karami and M.Yazdi and G. Mercier}
}
@article {1458,
	title = {Noise Reduction of Hyperspectral Images Using Kernel Nonnegative Tucker Decomposition},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {5},
	year = {2011},
	chapter = {487},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@proceedings {1466,
	title = {Best Rank-r Tensor Selection Using Genetic Algorithm for Better Noise Reduction and Compression of Hyperspectral Images},
	year = {2010},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@article {1460,
	title = {Compression and Noise Reduction of Hyperspectral Images Using Hybrid Genetic Algorithm and Nonnegative Tucker Decomposition},
	journal = {International Journal of Information studies },
	volume = {2},
	number = {10},
	year = {2010},
	chapter = {207},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@proceedings {1465,
	title = {Hyperspectral Image Compression Based on Tucker Decomposition and Discrete Cosine Transform},
	year = {2010},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@article {1462,
	title = {Voltage Security Enhancement and Congestion Management via STATCOM \& IPFC Using Artificial Intelligence},
	journal = {Iranian Journal of Science and Technology},
	volume = {1},
	number = {12},
	year = {2007},
	chapter = {289},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1468,
	title = {Application of RGA to Optimal Choice and Allocation of UPFC for Voltage Security Enhancement in Deregulated Power System},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@article {1461,
	title = {Coordination of UPFC \& SVC for Voltage Security Enhancement},
	journal = {WSEAS Transactions on Circuits and Systems},
	volume = {5},
	number = {6},
	year = {2006},
	chapter = {740},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1467,
	title = {Optimal Location of STATCOM for Voltage Security Enhancement via Artificial Intelligent},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1469,
	title = {Voltage Security Enhancement by Optimal FACTS Location via RGA},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
