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Jie Rui

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Proceedings Article | 13 June 2024 Paper

SSTNet: spatial-spectral transformer network for panchromatic sharpening

Shuxiang Wang, Fei Jin, Jie Rui, Yuzhun Lin, Yueqin Niu

Proceedings Volume 13180, 131806U (2024) https://doi.org/10.1117/12.3033635

KEYWORDS: Image fusion, Image sharpness, Image quality, Transformers, Deep learning, Remote sensing, Multispectral imaging

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In the field of remote sensing, panchromatic sharpening technology integrates spatial data from panchromatic images with spectral data from multispectral images to generate high-resolution multispectral images. Precise mapping from multispectral to single-band panchromatic images greatly impacts the quality of fusion images. This paper introduces the spatial-spectral transformer network (SSTNet). SSTNet combines the spatial constraints of the difference model in the gradient domain and the intensity constraints in the spectral domain to quantitatively express multispectral imagery into panchromatic imagery in a many-on-one form, laying the groundwork for designing the loss function in image fusion. compared to the original method, SSTNet is applied to P2Sharpen. Experiments on the Quickbird dataset demonstrate that the evaluation indexes of the SSTNet-based P2Sharpen method are improved in both reduced and full resolution resolution, requiring fewer training data and epoch.

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