@inproceedings{f1446cd75031400ca0ebe585e488a54f,
title = "Hyperspectral image resolution enhancement based on joint sparsity spectral unmixing",
abstract = "Relatively low spatial resolution of the space-borne hyper-spectral images (HSI) is the main drawback to derive value added products. Recently, several techniques have been proposed in order to enhance the spatial resolution HSI by means of fusion with higher spatial resolution multispectral images. This paper presents an alternative approach based on the joint sparsity model for spectral unmixing with the use of a-priori spectral dictionary. To assess the results, we compare our algorithm with the state of the art methods.",
keywords = "Hyperspectral image, image fusion, resolution enhancement, sparse unmixing",
author = "Jakub Bieniarz and Rupert Muller and Zhu, {Xiao Xiang} and Peter Reinartz",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",
year = "2014",
month = nov,
day = "4",
doi = "10.1109/IGARSS.2014.6947017",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2645--2648",
booktitle = "International Geoscience and Remote Sensing Symposium (IGARSS)",
}