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Journal of Applied Remote Sensing • Open Access

Spatial resolution enhancement of hyperspectral images based on redundant dictionaries
Author(s): Suyu Wang; Bo Wang; Zongxiang Zhang

Paper 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 <italic<a posteriori</italic< based constrained optimization is performed to further improve the quality of the reconstructed high-frequency information. Experimental results show that the pixel curve based sparse representation is more suitable for a hyperspectral image; the highly spectral correlations are better used for resolution enhancement. In comparison with the traditional bilinear interpolation method and other referenced super-resolution algorithms, the proposed algorithm is superior in both objective and subjective results.

Paper Details

Date Published: 26 August 2015
PDF: 10 pages
J. Appl. Remote Sens. 9(1) 097492 doi: 10.1117/1.JRS.9.097492
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Suyu Wang, Beijing Univ. of Technology (China)
Beijing Engineering Research Ctr. for IOT Software and System (China)
Bo Wang, Beijing Univ. of Technology (China)
Beijing Engineering Research Ctr. for IOT Software and System (China)
Zongxiang Zhang, Beijing Univ. of Technology (China)
Beijing Engineering Research Ctr. for IOT Software and System (China)


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