@inproceedings{4ed2479ee99a46a49b052827a67ebe69,
title = "Hiperspektral G{\"o}r{\"u}nt{\"u}lerde S{\"u}per{\c c}{\"o}z{\"u}n{\"u}rl{\"u}k",
abstract = "Despite their high spectral resolution, hyperspectral images have low spatial resolution which adversely affects the applications that use hyperspectral images. In this study, instead of the traditional way of using spectral images, abundances of the endmembers are used in resolution enhancement. In the proposed method, first, endmembers are extracted with the SISAL algorithm. Then, the abundance maps are estimated using FCLS. From the low resolution abundance maps, high resolution abundance maps are obtained with a total variation based minimization. Finally, high resolution hyperspectral images are constructed from high resolution abundance maps. The proposed method is tested on real hyperspectral images. The experimental results and comparative analysis show the effectiveness of the proposed method.",
keywords = "Abundance Maps, Endmember, Hyperspectral, Total Variation Minimization",
author = "Hasan Irmak and Akar, \{G{\"o}zde Bozdaǧi\} and Y{\"u}ksel, \{Seniha Esen\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 24th Signal Processing and Communication Application Conference, SIU 2016 ; Conference date: 16-05-2016 Through 19-05-2016",
year = "2016",
month = jun,
day = "20",
doi = "10.1109/SIU.2016.7495925",
language = "T{\"u}rk{\c c}e",
series = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1057--1060",
booktitle = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",
address = "!!United States",
}