@article {1379,
	title = {Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	month = {2013},
	pages = {779 - 791},
	abstract = {This paper describes a hyperspectral image classification method to obtain classification maps at a finer resolution than the image{\textquoteright}s original resolution. We assume that a complementary color image of high spatial resolution is available. The proposed methodology consists of a soft classification procedure to obtain landcover fractions, followed by a subpixel mapping of these fractions. While the main contribution of this article is in fact the complete multisource framework for obtaining a subpixel map, the major novelty of this subpixel mapping approach is the inclusion of contextual information, obtained from the color image. Experiments, conducted on two hyperspectral images and one real multi source data set, show excellent results, when compared to classification of the hyperspectral data only. The advantage of the contextual approach, compared to conventional subpixel mapping approaches, is clearly demonstrated.},
	keywords = {fusion, hyperspectral data, spectral unmixing, subpixel mapping, superresolution},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2012.2236539},
	author = {Zahid Mahmood and Muhamed Awais Akhter and Guy Thoonen and Paul Scheunders}
}
@mastersthesis {2369,
	title = {Hyperspectral data fusion for classification and visualization in remote sensing},
	year = {2013},
	type = {PhD thesis},
	author = {Zahid Mahmood}
}
@inproceedings {1508,
	title = {Hyperspectral image subpixel mapping using Getis index},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013},
	year = {2013},
	author = {Muhamed Awais Akhter and Zahid Mahmood and Paul Scheunders}
}
@inproceedings {1352,
	title = {Automatic threshold selection for morphological attribute profiles},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {4946-4949},
	author = {Zahid Mahmood and Guy Thoonen and Paul Scheunders}
}
@article {ThoonenMahmoodPeeterspscheund2012,
	title = {Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion},
	journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of},
	volume = {5},
	number = {2},
	year = {2012},
	pages = {510 - 521},
	abstract = {In this work, we treat the problem of combined classification of a high spatial resolution color image and a lower spatial resolution hyperspectral image of the same scene. The problem is particularly challenging, since we aim for classification maps at the spatial resolution of the color image. Contextual information is obtained from the color image by introducing Color Attribute Profiles (CAPs). Instead of treating the {\textquoteleft}R{\textquoteright}, {\textquoteleft}G{\textquoteright}, and {\textquoteleft}B{\textquoteright} bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, which corresponds to the Luminance, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands, which contain the color information, are kept intact, and the resulting images are transformed back into RGB space. The spectral information is obtained from the hyperspectral image. A Composite Decision Fusion (CDF) strategy is proposed, combining a state-of-the-art kernel-based decision fusion technique with the popular composite kernel classification approach. Experiments are conducted, using simulated data and a real multisource dataset containing airborne hyperspectral data and orthophotographic data from a suburban area in Belgium. These experiments show that our CAPs perform well with respect to other approaches for extracting attribute profiles from high resolution color images, and that the proposed CDF strategy produces meaningful results with respect to concatenation and the highlighted state-of-the-art approaches for combining multisource data.},
	keywords = {Color, image classification, morphological operations, multiresolution techniques, multisensor systems},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2011.2168317},
	author = {Guy Thoonen and Zahid Mahmood and Stijn Peeters and Paul Scheunders}
}
@inproceedings {1507,
	title = {Subpixel mapping of hyperspectral data using high resolution color images},
	booktitle = {IEEE-WHISPERS 2012, Workshop on Hyperspectral Image and Signal Processing, Shanghai, June 4-7},
	year = {2012},
	author = {Zahid Mahmood and Guy Thoonen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {Mahmoodpscheund2011,
	title = {Enhanced visualization of hyperspectral images},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {8},
	number = {5},
	year = {2011},
	pages = {869-873},
	author = {Zahid Mahmood and Paul Scheunders}
}
@inproceedings {MahmoodWittepscheund2011,
	title = {Multi-source image classification using color attribute profiles},
	booktitle = {3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2011},
	pages = {1 - 4},
	address = {Lisbon, Portugal},
	abstract = {This work introduces a method to extract attribute profiles from RGB color images with high spatial resolution, for instance images acquired from Unmanned Aerial Vehicles (UAV). The resulting Color Attribute Profiles (CAP) are intended to improve the classification of low spatial resolution hyperspectral images by merging the attribute features with the spectral features of the hyperspectral image. Instead of treating the R, G and B bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands are kept intact, and the resulting images are transformed back into RGB space. In our experiments, classification results using this methodology are compared to classification results using other strategies for extracting attribute profiles in CIE-Lab space, as well as regular grayscale attribute profiles.},
	keywords = {Attributes, Classification, Color, Hyperspectral, Morphology},
	isbn = {978-1-4577-2202-8},
	issn = {2158-6268},
	doi = {10.1109/WHISPERS.2011.6080859},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6080859\&isnumber=6080842},
	author = {Zahid Mahmood and Guy Thoonen and Val{\'e}rie De Witte and Paul Scheunders}
}
@inproceedings {5652813,
	title = {Enhanced visualization of hyperspectral images},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International},
	year = {2010},
	month = {july},
	pages = {991 -994},
	keywords = {color matching, data visualisation, geophysical image processing, gradient methods, human vision system, hyperspectral image, hyperspectral visualization algorithm, image colour analysis, image matching, image resolution, multiband gradient information, multiresolution framework, multiscale fundamental form representation, wavelet transforms, wavelets},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2010.5652813},
	author = {Zahid Mahmood and Paul Scheunders}
}
