@inproceedings{ae42b5b7f74b49989fde154ec342873b,
title = "Hyperspectral imagery super-resolution by image fusion and compressed sensing",
abstract = "Low spatial resolution is the mainly drawback of hyperspectral imaging. Image super-resolution techniques can be applied to overcome the limits. This paper presents a new framework for improving the spatial resolution of hyperspectral images base by combing high-resolution spectral information and high-resolution spatial information by image fusion and compressed sensing. Based on the compressed sensing theory, small patches of hyperspectral observations from different wavelengths can be represented as weighted linear combinations of a small number of atoms in dictionary which is trained by using panchromatic images. Then hyperspectral image super-resolution is treated as a special image fusion problem with sparse constraints. To make the super-resolution reconstruction more accurate, local manifold projection is used as a regulation term. Extensive experiments on image super-resolution validate that proposed method achieves much better results.",
keywords = "Hyperspectral, image fusion, sparse representation, super-resolution reconstruction",
author = "Yongqiang Zhao and Yaozhong Yang and Qingyong Zhang and Jinxiang Yang and Jie Li",
year = "2012",
doi = "10.1109/IGARSS.2012.6351986",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7260--7262",
booktitle = "IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium",
note = "32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 ; Conference date: 22-07-2012 Through 27-07-2012",
}