Image Fusion Based on Kernel Estimation and Data Envelopment Analysis

Qiwei Xie, Xi Chen, Lin Li, Kaifeng Rao, Luo Tao and Chao Ma
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Qiwei Xie: Data Mining Lab, School of Economics and Management, Beijing University of Technology, Beijing 100124, P. R. China†Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China
Xi Chen: Data Mining Lab, School of Economics and Management, Beijing University of Technology, Beijing 100124, P. R. China
Lin Li: Academy of Mathematics and System Science, Chinese Academy of Sciences, Beijing 100190, P. R. China
Kaifeng Rao: Research Center for Eco-Environment Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
Luo Tao: Tianjin Key Laboratory of Cognitive Computing and Applications, School of Computer Science and Technology, Tianjin University, Tianjin 300072, P. R. China
Chao Ma: Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 02, 487-515

Abstract: This paper reports the improvement of the image quality during the fusion of remote sensing images by minimizing a novel energy function. First, by introducing a gradient constraint term in the energy function, the spatial information of the panchromatic image is transferred to the fused results. Second, the spectral information of the multispectral image is preserved by importing a kernel function to the data fitting term in the energy function. Finally, an objective parameter selection method based on data envelopment analysis (DEA) is proposed to integrate state-of-the-art image quality metrics. Visual perception measurement and selected fusion metrics are employed to evaluate the fusion performance. Experimental results show that the proposed method outperforms other established image fusion techniques.

Keywords: Data envelopment analysis; image fusion; mutual information (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219622019500032

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