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MvSSIM: A quality assessment index for hyperspectral images

Zhu, Rui, Zhou, Fei, Xue, Jing-Hao (2017) MvSSIM: A quality assessment index for hyperspectral images. Neurocomputing, 272 . pp. 250-257. ISSN 0925-2312. (doi:10.1016/j.neucom.2017.06.073) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:63940)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
http://dx.doi.org/10.1016/j.neucom.2017.06.073

Abstract

Quality assessment indexes play a fundamental role in the analysis of hyperspectral image (HSI) cubes. To assess the quality of an HSI cube, the structural similarity (SSIM) index has been widely applied in a band-by-band manner, as SSIM was originally designed for 2D images, and then the mean SSIM (MeanSSIM) index over all bands is adopted. MeanSSIM fails to accommodate the spectral structure which is a unique characteristic of HSI. Hence in this paper, we propose a new and simple multivariate SSIM (MvSSIM) index for HSI, by treating the pixel spectrum as a multivariate random vector. MvSSIM maintains SSIM’s ability to assess the spatial structural similarity via correlation between two images of the same band; and adds an ability to assess the spectral structural similarity via covariance among different bands. MvSSIM is well founded on multivariate statistics and can be easily implemented through simple sample statistics involving mean vectors, covariance matrices and cross-covariance matrices. Experiments show that MvSSIM is a proper quality assessment index for distorted HSIs with different kinds of degradations.

Item Type: Article
DOI/Identification number: 10.1016/j.neucom.2017.06.073
Uncontrolled keywords: Hyperspectral images; Quality assessment; Structural similarity (SSIM); Spectral structure; Spatial structure
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: R. Zhu
Date Deposited: 10 Oct 2017 20:23 UTC
Last Modified: 04 Mar 2024 17:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63940 (The current URI for this page, for reference purposes)

University of Kent Author Information

Zhu, Rui.

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