SSTNet: spatial-spectral transformer network for panchromatic sharpening

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

doi:10.1117/12.3033635

13 June 2024 SSTNet: spatial-spectral transformer network for panchromatic sharpening

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

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131806U (2024) https://doi.org/10.1117/12.3033635
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuxiang Wang, Fei Jin, Jie Rui, Yuzhun Lin, and Yueqin Niu "SSTNet: spatial-spectral transformer network for panchromatic sharpening", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131806U (13 June 2024); https://doi.org/10.1117/12.3033635

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KEYWORDS

Image fusion

Image sharpness

Image quality

Deep learning

Transformers

Remote sensing

Multispectral imaging

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