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A pixel-level fusion method for multi-source optical remote sensing image combining the principal component analysis and curvelet transform

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Abstract

With the availability of multi-sensor, multi-temporal, multi-resolution and multi-spectral images from operational Earth observation satellites, remote sensing image fusion has become a valuable tool. The goal of remote sensing image fusion is to integrate complementary information from multi-source data such that the new images are more suitable for human visual perception and computer-processing tasks such as segmentation, feature extraction, and object recognition. In this paper, a pixel-level remote sensing image fusion method is proposed, which is based on combining the principal component analysis (PCA) and the curvelet transformation (CT). First, the multi-spectral image with low-spatial-resolution is transformed by PCA and principal components are obtained. Second, the panchromatic image with high-spatial-resolution and the principal components of the multi-spectral image are respectively merged with the curvelet transform. Finally, the fused image is obtained by inverse CT and inverse PCA. The experiments using Landsat-8 OLI multi-spectral and panchromatic image show that, compared with the traditional methods such as the WT-based method, the IHS-based method, the HPF-based method, the BT-based method, the PCA-based method and the CT-based method, the results of the proposed method preserve the spatial details while preserving more spectral information of the original image.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (41701447), the Fundamental Research Funds for Zhejiang Provincial Universities and Research Institutes (2019J00003), the Training Program of Excellent Master Thesis of Zhejiang Ocean University; the State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University (2018-KF-02).

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Authors and Affiliations

  1. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, China

    Chao Chen, Xinyue He & Biyun Guo

  2. Taishan College of Science and Technology, Shandong University of Science and Technology, Tai’an, China

    Xin Zhao

  3. Geomatics College, Shandong University of Science and Technology, Qingdao, China

    Xin Zhao

  4. School of Economics and Management, Zhejiang Ocean University, Zhoushan, China

    Yanli Chu

Authors
  1. Chao Chen
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  2. Xinyue He
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  3. Biyun Guo
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  4. Xin Zhao
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  5. Yanli Chu
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Corresponding author

Correspondence to Biyun Guo.

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Communicated by: H. Babaie

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Chen, C., He, X., Guo, B. et al. A pixel-level fusion method for multi-source optical remote sensing image combining the principal component analysis and curvelet transform. Earth Sci Inform 13, 1005–1013 (2020). https://doi.org/10.1007/s12145-020-00472-7

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  • DOI: https://doi.org/10.1007/s12145-020-00472-7

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