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Multispectral and multiresolution image fusion using particle swarm optimization

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Abstract

Multispectral and multiresolution image fusion is important for many multimedia and remote sensing applications, such as video surveillance, medical imaging, and satellite imaging. For the commercial satellite “IKONOS,” spatial resolutions of high-resolution panchromatic (PAN) and low-resolution multispectral (MS) satellite images are 1 m and 4 m, respectively. To cope with color distortion and blocking artifacts in fused images, in this study, a multispectral and multiresolution image fusion approach using PSO is proposed. The pixels of fused images in the training set are classified into several categories based on the characteristics of low-resolution MS images. Then, the smooth parameters of spatial and spectral responses between the high-resolution PAN and low-resolution MS images are determined by PSO. All the pixels within each category are normalized by its own smooth parameter so that color distortion and blocking artifacts can be greatly reduced. Based on the experimental results obtained in this study, the overall visual quality of the fused images by the proposed approach is better than that by three comparison approaches, whereas the correlation coefficients, γ PAN , for the fused images by the proposed approach are greater than that by three comparison approaches.

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Acknowledgment

The authors would like to thank the Geoeye Company for providing the IKONOS PAN and MS images used in this study.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, 621, Taiwan

    Hsuan-Ying Chen & Jin-Jang Leou

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  1. Hsuan-Ying Chen
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  2. Jin-Jang Leou
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Corresponding author

Correspondence to Jin-Jang Leou.

Additional information

This work was supported in part by National Science Council, Taiwan, Republic of China under Grants NSC 98-2221-E-194-034-MY3 and NSC 99-2221-E-194-032-MY3.

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Chen, HY., Leou, JJ. Multispectral and multiresolution image fusion using particle swarm optimization. Multimed Tools Appl 60, 495–518 (2012). https://doi.org/10.1007/s11042-011-0820-3

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