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A Hybrid Pan-Sharpening Approach Using Nonnegative Matrix Factorization for WorldView Imageries

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Book cover Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

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Abstract

With the advent of WorldView series imageries (WorldView-2/3/4), it is necessary to develop new fusion approaches for remote sensing images with higher spatial and spectral resolutions. Since most existing fusion approaches are not well capable of merging multi-spectral images with eight bands, a new hybrid pan-sharpening approach is proposed in this paper. The hybrid framework integrates the multiplicative model and additive model to improve the quality of multi-spectral images. In the additive procedure, the nonnegative matrix factorization (NMF) algorithm is utilized to synthesize the intensity component for obtaining the mutual information from multi-spectral images. Then the difference information between the panchromatic image and synthetic component is injected into multi-spectral images by the spectral-adjustable weights. In the multiplicative procedure, the smoothing filter-based intensity modulation (SFIM) is used to modulate the preliminary fusion. The nonlinear fitting method is utilized to calculate the optimal parameters of the hybrid model. Visual and quantitative assessments of fused images show that the proposed approach clearly improves the fusion quality compared to the state-of-the-art algorithms.

Supported by the National Nature Science Foundation of China (NO.61402368 and NO.61702419), Aerospace Support Fund (2017-HT-XGD), Aerospace Science and Technology Innovation Foundation (2017 ZD 53047).

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Shaanxi, 710072, China

    Guiqing He, Jiaqi Ji, Qiqi Zhang & Zhaoqiang Xia

Authors
  1. Guiqing He
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  2. Jiaqi Ji
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  3. Qiqi Zhang
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  4. Zhaoqiang Xia
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Corresponding author

Correspondence to Guiqing He .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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He, G., Ji, J., Zhang, Q., Xia, Z. (2019). A Hybrid Pan-Sharpening Approach Using Nonnegative Matrix Factorization for WorldView Imageries. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_60

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  • DOI: https://doi.org/10.1007/978-3-030-31723-2_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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