@article {1934,
	title = {Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	chapter = {624},
	abstract = {Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple feature sets and decisions from individual classifiers to be employed in a joint manner. In this work, we present a flexible decision fusion framework addressing these issues. In a first step, we propose to use sparse fractional abundances as decision source, complementary to class probabilities obtained from a supervised classifier. This specific selection of complementary decision sources enables the description of a pixel in a more complete way, and is expected to mitigate the effects of small training samples sizes. Secondly, we propose to apply a fusion scheme, based on the probabilistic graphical Markov Random Field (MRF) and Conditional Random Field (CRF) models, which inherently employ spatial information into the fusion process. To strengthen the decision fusion process, consistency links across the different decision sources are incorporated to encourage agreement between their decisions. The proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward way. Experimental results conducted on two real hyperspectral images show superiority over several other approaches in terms of classification performance when very limited training data is available},
	keywords = {Classification, decision fusion, Hyperspectral},
	doi = {https://doi.org/10.3390/rs11060624},
	url = {https://www.mdpi.com/2072-4292/11/6/624},
	author = {Vera Andrejchenko and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@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 {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 {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}
}
