@inproceedings {1521,
	title = {Spectral adaptation of hyperspectral flight lines using VHR contextual information},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International},
	year = {2014},
	pages = {2953-2956},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Quebec City, QC, Canada},
	abstract = {Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant textural features for improving the performance of the used Hidden Markov Random Field matching framework in the case of hyperspectral flight lines. These textural features are derived from the filtering of VHR optical imagery with a bank of Gabor filters with varying orientation, scale and frequency and subsequently rendering them invariant to scale and frequency by applying the 2D DFT on the filter responses in the scale and frequency space.},
	doi = {10.1109/IGARSS.2014.6947096},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6947096},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Pieter Kempeneers and Paul Scheunders}
}
@inproceedings {Jacobs13,
	title = {Domain adaptation with Hidden Markov Random Fields},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International},
	year = {2013},
	month = {July},
	pages = {3112-3115},
	address = {Melbourne, VIC, Australia},
	abstract = {In this paper, we propose a method to match multitemporal sequences of hyperspectral images using Hidden Markov Random Fields. Based on the matching of the data manifold, the algorithm matches the reflectance spectra of the classes, thus allowing the reuse of labeled examples acquired on one image to classify the other. This allows valorization of spectra collected in situ to other acquisitions than the one they were acquired for, without user supervision, prior knowledge of the class reflectance in the new domain or global information about atmospheric conditions.},
	keywords = {domain adaptation, graph matching, Hidden Markov Random Fields, Multitemporal classification},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2013.6723485},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Birgen Haest and Paul Scheunders}
}
@article {1410,
	title = {Hyperspectral remote sensing data analysis and future challenges},
	journal = {IEEE Geoscience and Remote Sensing Magazine},
	volume = {1},
	year = {2013},
	pages = {6-36},
	author = {Jose Bioucas-Dias and Antonio Plaza and Gustavo Camps-Valls and Paul Scheunders and Nasser Nasrabadi and Jocedlyn Chanussot}
}
