Remote Sensing Applications and Decision Support

Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient

[+] Author Affiliations
Dan Ma

Fujian Agriculture and Forestry University, College of Resources and Environment, Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation, No. 15 Shangxiadian Road, Cangshan District, Fuzhou 350002, China

Jun Liu, Kai Chen, Ping Liu, Huijuan Chen, Jing Qian

Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, No. 1068 Xueyuan Road, Nanshan District, Shenzhen 518055, China

Huali Li

Hunan University, College of Electrical and Information Engineering, Lushan Gate Lushan South Road, Yuelu District, Changsha, Hunan 410082, China

J. Appl. Remote Sens. 10(2), 026005 (Apr 12, 2016). doi:10.1117/1.JRS.10.026005
History: Received November 17, 2015; Accepted March 23, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Dan Ma ; Jun Liu ; Kai Chen ; Huali Li ; Ping Liu, et al.
"Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient", J. Appl. Remote Sens. 10(2), 026005 (Apr 12, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.026005


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