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    Rights statement: This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 330, 2019 DOI: 10.1016/j.neucom.2018.11.039

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Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation

Research output: Contribution to journalJournal article

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  • Y. Liu
  • C. Shan
  • Q. Gao
  • X. Gao
  • J. Han
  • R. Cui
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<mark>Journal publication date</mark>22/02/2019
<mark>Journal</mark>Neurocomputing
Volume330
Number of pages18
Pages (from-to)465-482
Publication StatusPublished
Early online date20/11/18
<mark>Original language</mark>English

Abstract

Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI's discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art. © 2018 Elsevier B.V.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 330, 2019 DOI: 10.1016/j.neucom.2018.11.039