26 August 2015 Spatial resolution enhancement of hyperspectral images based on redundant dictionaries
Author Affiliations +
Abstract
Spatial resolution enhancement of hyperspectral images is one of the key and difficult topics in the field of imaging spectrometry. The redundant dictionary based sparse representation theory is introduced, and a spatial resolution enhancement algorithm is proposed. In this algorithm, a pixel curve instead of a pixel patch is taken as the unit of processing. A pair of low- and high-resolution respective redundant dictionaries are joint trained, with the constraint that a pair of high- and low-resolution corresponded pixel curves can be sparse represented by same coefficients according to the respected dictionaries. In the process of super-resolution restoration, the low-resolution hyperspectral image is first sparse decomposed based on the low-resolution redundant dictionary and then the obtained coefficients are used to reconstruct the corresponding high-resolution image with respect to the high-resolution dictionary. The maximum
Wang, Wang, and Zhang: Spatial resolution enhancement of hyperspectral images based on redundant dictionaries
© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Suyu Wang, Bo Wang, Zongxiang Zhang, "Spatial resolution enhancement of hyperspectral images based on redundant dictionaries," Journal of Applied Remote Sensing 9(1), 097492 (26 August 2015). https://doi.org/10.1117/1.JRS.9.097492 . Submission:
JOURNAL ARTICLE
10 PAGES


SHARE
Back to Top