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Báo cáo hóa học: "Hyperspectral imagery super-resolution by sparse representation and spectral regularization"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài :Hyperspectral imagery super-resolution by sparse representation and spectral regularization | Zhao et al. EURASIP Journal on Advances in Signal Processing 2011 2011 87 http asp.eurasipjournals.eom content 2011 1 87 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Hyperspectral imagery super-resolution by sparse representation and spectral regularization Yongqiang Zhao Jinxiang Yang Qingyong Zhang Lin Song Yongmei Cheng and Quan Pan 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. Keywords hyperspectral sparse representation super-resolution linear mixing model 1. Introduction Hyperspectral sensor can acquire imagery in many contiguous and very narrow such as 10 nm spectral bands