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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(2): 391-404
Y. Jia, Q. Hu, R. Dian, J. Ma, and  X. Guo,  “PAPS: Progressive attention-based pan-sharpening,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 391–404, Feb. 2024. doi: 10.1109/JAS.2023.123987
Citation: Y. Jia, Q. Hu, R. Dian, J. Ma, and  X. Guo,  “PAPS: Progressive attention-based pan-sharpening,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 391–404, Feb. 2024. doi: 10.1109/JAS.2023.123987
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PAPS: Progressive Attention-Based Pan-sharpening

doi: 10.1109/JAS.2023.123987
Funds:  This work was partially supported by the National Natural Science Foundation of China (62372251)
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    Abstract

  • Pan-sharpening aims to seek high-resolution multispectral (HRMS) images from paired multispectral images of low resolution (LRMS) and panchromatic (PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS images. Having a better MS base (enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of constructing the desired HRMS. Extensive experiments together with ablation studies on widely-used datasets are provided to verify the efficacy of our design, and demonstrate its superiority over other state-of-the-art methods both quantitatively and qualitatively. Our code has been released at

    https://github.com/JiaYN1/PAPS

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    Highlights

    • A progressive attention-based network for pan-sharpening is designed
    • The detail enhancement module is introduced to provide better multispectral references for fusion
    • The progressive fusion module is proposed to take full advantage of spectral and spatial information
    • The proposed method can flexibly reduce the parameters and produce appealing results

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