A Novel Rank Approximation Method for Mixture Noise Removal of Hyperspectral Images

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Author(s)

  • Hailiang Ye
  • Hong Li
  • Bing Yang
  • Feilong Cao
  • Yuanyan Tang

Detail(s)

Original languageEnglish
Article number8632962
Pages (from-to)4457-4469
Journal / PublicationIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number7
Online published1 Feb 2019
Publication statusPublished - Jul 2019

Abstract

Mixture noise removal is a fundamental problem in hyperspectral images' (HSIs) processing that holds significant practical importance for subsequent applications. This problem can be recast as an approximation issue of a low-rank matrix. In this paper, a novel smooth rank approximation (SRA) model is proposed to cope with these mixture noises for HSIs. The crux idea is to devise a general smooth function under some assumptions to directly approximate the rank function, which attempts to explore a closer approximation than conventional methods. This new optimization model can be easily solved by the convex analysis tool and can remove the mixture noises of HSIs quickly and effectively. Subsequently, we give a feasible iterative algorithm, and the corresponding convergence analysis is discussed mathematically. Experimental results from the simulated data set as well as real data sets illustrate that the proposed SRA method significantly outperforms the state-of-the-art methods on HSI denoising.

Research Area(s)

  • Denoising, hyperspectral images (HSIs), low rank, remote sensing, smooth approximation

Citation Format(s)

A Novel Rank Approximation Method for Mixture Noise Removal of Hyperspectral Images. / Ye, Hailiang; Li, Hong; Yang, Bing; Cao, Feilong; Tang, Yuanyan.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 7, 8632962, 07.2019, p. 4457-4469.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review