Hyperspectral image fusion based on dual-resolution fusion feature mutual guidance network

Lu Liu; Kai Ma; Jing Yan; Fulai Xing; Wanqing Zhao; Shenglin Peng; Lin Wang

doi:10.1117/12.2673754

9 August 2023 Hyperspectral image fusion based on dual-resolution fusion feature mutual guidance network

Lu Liu, Kai Ma, Jing Yan, Fulai Xing, Wanqing Zhao, Shenglin Peng, Lin Wang

Author Affiliations +

Proceedings Volume 12620, Optics for Arts, Architecture, and Archaeology (O3A) IX; 126200H (2023) https://doi.org/10.1117/12.2673754
Event: SPIE Optical Metrology, 2023, Munich, Germany

Conference Poster

Abstract

In recent years, the applications of hyperspectral imaging in the protection and analysis of cultural relics have received widespread attention. However, due to the limitation of imaging sensors, the spatial resolution of existing hyperspectral images is low, which hinders the development of hyperspectral digitization of cultural relics. Hyperspectral (HS) and RGB image fusion technology can generate hyperspectral images with high spatial resolution, which has gradually become a research hotspot. Inspired by the astounding performance of deep learning in various hyperspectral image processing tasks, this paper proposes a hyperspectral image fusion method based on dual-resolution fusion feature mutual guidance network (DRFFMG). Firstly, two feature extraction networks for HS and RGB images with different resolution pairs are designed to increase the richness of extracted features and reduce the loss of original hyperspectral information. Then, the spatial and spectral features extracted from the above feature extraction networks are fused, and a fusion feature mutual guidance module is designed to promote the mutual learning of different spatial features through information transmission, effectively reducing spatial distortion. Finally, the desired high spatial resolution HS image is restored from the fused features through an image reconstruction network. Experiments demonstrate that the proposed DRFFMG network can produce fusion images competitive with even better to state of the arts, and retain spectral information while improving spatial resolution.

Conference Presentation

Citation Download Citation

Lu Liu, Kai Ma, Jing Yan, Fulai Xing, Wanqing Zhao, Shenglin Peng, and Lin Wang "Hyperspectral image fusion based on dual-resolution fusion feature mutual guidance network", Proc. SPIE 12620, Optics for Arts, Architecture, and Archaeology (O3A) IX, 126200H (9 August 2023); https://doi.org/10.1117/12.2673754

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