@inproceedings{3e6b127692664082b304391cdffac84a,
title = "The J-SparseFI-HM Hyperspectral resolution enhancement method - Now fully automated",
abstract = "This paper introduces the new scheme of the previously proposed Joinly Sparse Fusion of Hyperspectral and Multispectral Imagery (J-SparseFI-HM) fusion method. This extended, now fully automated and parallelized version of J-SparseFI-HM jointly estimates bundles of an adjustable number of high resolution hyperspectral bands by fusing corresponding low resolution bands with possibly multiple high resolution multispectral ones. Which multispectral bands are individually used is decided via a decision matrix that is calculated from the spectral response functions of the multispectral sensor. Tests are performed on the SuperMUC petascale system. Recently acquired 0.75 airborne VNIR HySpex data is used to synthesize a WorldView-2 image as well as low resolution hyperspectral data with a down-sampling factor of 10. The fusion results are compared to those produced by three state-of-the-are hyperspectral resolution enhancement methods.",
keywords = "HySpex, J-SparseFI-HM, image fusion, joint sparse representation",
author = "Claas Grohnfeldt and Zhu, {Xiao Xiang} and Richard Bamler",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 ; Conference date: 24-06-2014 Through 27-06-2014",
year = "2014",
month = jun,
day = "28",
doi = "10.1109/WHISPERS.2014.8077507",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2014 6th Workshop on Hyperspectral Image and Signal Processing",
}