Hyperspectral image denoising via sparse representation and low-rank constraint

Yong Qiang Zhao, Jingxiang Yang

科研成果: 期刊稿件文章同行评审

361 引用 (Scopus)

摘要

Hyperspectral image (HSI) denoising is an essential preprocess step to improve the performance of subsequent applications. For HSI, there is much global and local redundancy and correlation (RAC) in spatial/spectral dimensions. In addition, denoising performance can be improved greatly if RAC is utilized efficiently in the denoising process. In this paper, an HSI denoising method is proposed by jointly utilizing the global and local RAC in spatial/spectral domains. First, sparse coding is exploited to model the global RAC in the spatial domain and local RAC in the spectral domain. Noise can be removed by sparse approximated data with learned dictionary. At this stage, only local RAC in the spectral domain is employed. It will cause spectral distortion. To compensate the shortcoming of local spectral RAC, low-rank constraint is used to deal with the global RAC in the spectral domain. Different hyperspectral data sets are used to test the performance of the proposed method. The denoising results by the proposed method are superior to results obtained by other state-of-the-art hyperspectral denoising methods.

源语言英语
文章编号2321557
页(从-至)296-308
页数13
期刊IEEE Transactions on Geoscience and Remote Sensing
53
1
DOI
出版状态已出版 - 1月 2015

指纹

探究 'Hyperspectral image denoising via sparse representation and low-rank constraint' 的科研主题。它们共同构成独一无二的指纹。

引用此