Share Email Print
cover

Journal of Applied Remote Sensing

Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient
Author(s): Dan Ma; Jun Liu; Kai Chen; Huali Li; Ping Liu; Huijuan Chen; Jing Qian
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In remote sensing fusion, the spatial details of a panchromatic (PAN) image and the spectrum information of multispectral (MS) images will be transferred into fused images according to the characteristics of the human visual system. Thus, a remote sensing image fusion quality assessment called feature-based fourth-order correlation coefficient (FFOCC) is proposed. FFOCC is based on the feature-based coefficient concept. Spatial features related to spatial details of the PAN image and spectral features related to the spectrum information of MS images are first extracted from the fused image. Then, the fourth-order correlation coefficient between the spatial and spectral features is calculated and treated as the assessment result. FFOCC was then compared with existing widely used indices, such as Erreur Relative Globale Adimensionnelle de Synthese, and quality assessed with no reference. Results of the fusion and distortion experiments indicate that the FFOCC is consistent with subjective evaluation. FFOCC significantly outperforms the other indices in evaluating fusion images that are produced by different fusion methods and that are distorted in spatial and spectral features by blurring, adding noise, and changing intensity. All the findings indicate that the proposed method is an objective and effective quality assessment for remote sensing image fusion.

Paper Details

Date Published: 12 April 2016
PDF: 12 pages
J. Appl. Remote Sens. 10(2) 026005 doi: 10.1117/1.JRS.10.026005
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Dan Ma, Fujian Agriculture and Forestry Univ. (China)
Jun Liu, Shenzhen Institutes of Advanced Technology (China)
Kai Chen, Shenzhen Institutes of Advanced Technology (China)
Huali Li, Hunan Univ. (China)
Ping Liu, Shenzhen Institutes of Advanced Technology (China)
Huijuan Chen, Shenzhen Institutes of Advanced Technology (China)
Jing Qian, Shenzhen Institutes of Advanced Technology (China)


© SPIE. Terms of Use
Back to Top