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

Yang Liu (Corresponding author), Caifeng Shan, Quanxue Gao (Corresponding author), Xinbo Gao, Jungong Han, Rongmei Cui

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)465-482
Number of pages18
JournalNeurocomputing
Volume330
DOIs
Publication statusPublished - 22 Feb 2019

Keywords

  • Denoising
  • Hyperspectral images
  • PSSV
  • Superpixel segmentation

Fingerprint

Dive into the research topics of 'Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation'. Together they form a unique fingerprint.

Cite this