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Hyperspectral imagery super-resolution by sparse representation and spectral regularization

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Author(s): Zhao Yongqiang | Yang Jinxiang | Zhang Qingyong | Song Lin | Cheng Yongmei | Pan Quan

Journal: EURASIP Journal on Advances in Signal Processing
ISSN 1687-6172

Volume: 2011;
Issue: 1;
Start page: 87;
Date: 2011;
Original page

Keywords: hyperspectral | sparse representation | super-resolution | linear mixing model

ABSTRACT
Abstract 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.

Tango Rapperswil
Tango Rapperswil

    
RPA Switzerland

RPA Switzerland

Robotic process automation