Dual branch self-attention DenseNet for pansharpening

Ying Wang; ShanShan Pan; Fang Zuo; Chen Wang

doi:10.1117/12.2625725

9 December 2021 Dual branch self-attention DenseNet for pansharpening

Ying Wang, ShanShan Pan, Fang Zuo, Chen Wang

Proceedings Volume 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021); 121290U (2021) https://doi.org/10.1117/12.2625725
Event: 2021 International Conference on Environmental Remote Sensing and Big Data, 2021, Wuhan, China

Abstract

The purpose of pansharpening is to fuse low-resolution multispectral image (LRMS) and high-resolution panchromatic image (PAN) to obtain high-resolution multispectral (HRMS). In response to the shortcomings of traditional remote sensing image fusion algorithms causing spectral distortion, more and more deep learning algorithms are utilized, and this paper proposes a new deep network structure, two-branch Self-Attentive DenseNet network. In terms of maintaining high spatial resolution, the image feature information is extracted by different inch-scale convolutional kernels, and the effective feature information is enhanced to suppress the invalid image information by using DenseNet network model and introducing Self-Attention, and the fused image spectral information is enhanced by using hopping connection to maintaining the spectral structure. Experiments show that the proposed method of this paper has improved image quality evaluation metrics compared with the previously existing traditional fusion algorithms and deep network algorithms.

Citation Download Citation

Ying Wang, ShanShan Pan, Fang Zuo, and Chen Wang "Dual branch self-attention DenseNet for pansharpening", Proc. SPIE 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021), 121290U (9 December 2021); https://doi.org/10.1117/12.2625725

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KEYWORDS

Image fusion

Feature extraction

Image enhancement

Convolution

Multispectral imaging

Wavelets

Distortion

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