Hyperspectral imagery super-resolution by sparse representation and spectral regularizationReport as inadecuate

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EURASIP Journal on Advances in Signal Processing

, 2011:87

First Online: 12 October 2011Received: 31 March 2011Accepted: 12 October 2011


For the instrument limitation and imperfect imaging optics, it is difficult to acquire high spatial resolution hyperspectral imagery. Low spatial resolution will result in a lot of mixed pixels and greatly degrade the detection and recognition performance, affect the related application in civil and military fields. As a powerful statistical image modeling technique, sparse representation can be utilized to analyze the hyperspectral image efficiently. Hyperspectral imagery is intrinsically sparse in spatial and spectral domains, and image super-resolution quality largely depends on whether the prior knowledge is utilized properly. In this article, we propose a novel hyperspectral imagery super-resolution method by utilizing the sparse representation and spectral mixing model. Based on the sparse representation model and hyperspectral image acquisition process model, small patches of hyperspectral observations from different wavelengths can be represented as weighted linear combinations of a small number of atoms in pre-trained dictionary. Then super-resolution is treated as a least squares problem with sparse constraints. To maintain the spectral consistency, we further introduce an adaptive regularization terms into the sparse representation framework by combining the linear spectrum mixing model. Extensive experiments validate that the proposed method achieves much better results.

Keywordshyperspectral sparse representation super-resolution linear mixing model Electronic supplementary materialThe online version of this article doi:10.1186-1687-6180-2011-87 contains supplementary material, which is available to authorized users.

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Author: Yongqiang Zhao - Jinxiang Yang - Qingyong Zhang - Lin Song - Yongmei Cheng - Quan Pan

Source: https://link.springer.com/


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