Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening

Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-based methods are hard to understand and lack inter...

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Bibliographic Details
Published in:Optics letters 2022-03, Vol.47 (6), p.1371-1374
Main Authors: Yang, Yufei, Dong, Wenqian, Xiao, Song, Zhang, Tongzhen, Qu, Jiahui
Format: Article
Language:English
Subjects:
Artificial neural networks
Neural networks
Neural Networks, Computer
Spatial resolution
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Summary:Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-based methods are hard to understand and lack interpretability due to the black-box design. In this Letter, we propose a multi-level spatial details cross-extraction and injection network (MSCIN) for HS pansharpening, which introduces the mature multi-resolution analysis (MRA) technology to the neural network. Following the general idea of MRA, the proposed MSCIN divides the pansharpening process into details extraction and details injection, in which the missing details and the injection gains are estimated by two specifically designed interpretable sub-networks. Experimental results on two widely used datasets demonstrate the superiority of the proposed method.
ISSN:0146-9592
1539-4794
DOI:10.1364/OL.447405