CCC-SSA-UNet: U-Shaped Pansharpening Network with Channel Cross-Concatenation and Spatial-Spectral Attention Mechanism for Hyperspectral Image Super-Resolution

doi:10.3390/rs15174328

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	CCC-SSA-UNet: U-Shaped Pansharpening Network with Channel Cross-Concatenation and Spatial-Spectral Attention Mechanism for Hyperspectral Image Super-Resolution
	Z. C. Liu, G. L. Han, H. Yang, P. X. Liu, D. B. Chen, D. X. Liu and A. P. Deng
刊名	Remote Sensing
	2023
卷号	15 期号:17 页码:34
DOI	10.3390/rs15174328
英文摘要	A hyperspectral image (HSI) has a very high spectral resolution, which can reflect the target's material properties well. However, the limited spatial resolution poses a constraint on its applicability. In recent years, some hyperspectral pansharpening studies have attempted to integrate HSI with PAN to improve the spatial resolution of HSI. Although some achievements have been made, there are still shortcomings, such as insufficient utilization of multi-scale spatial and spectral information, high computational complexity, and long network model inference time. To address the above issues, we propose a novel U-shaped hyperspectral pansharpening network with channel cross-concatenation and spatial-spectral attention mechanism (CCC-SSA-UNet). A novel channel cross-concatenation (CCC) method was designed to effectively enhance the fusion ability of different input source images and the fusion ability between feature maps at different levels. Regarding network design, integrating a UNet based on an encoder-decoder architecture with a spatial-spectral attention network (SSA-Net) based on residual spatial-spectral attention (Res-SSA) blocks further enhances the ability to extract spatial and spectral features. The experiment shows that our proposed CCC-SSA-UNet exhibits state-of-the-art performance and has a shorter inference runtime and lower GPU memory consumption than most of the existing hyperspectral pansharpening methods.
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语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/67740]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Z. C. Liu, G. L. Han, H. Yang, P. X. Liu, D. B. Chen, D. X. Liu and A. P. Deng. CCC-SSA-UNet: U-Shaped Pansharpening Network with Channel Cross-Concatenation and Spatial-Spectral Attention Mechanism for Hyperspectral Image Super-Resolution[J]. Remote Sensing,2023,15(17):34.
APA	Z. C. Liu, G. L. Han, H. Yang, P. X. Liu, D. B. Chen, D. X. Liu and A. P. Deng.(2023).CCC-SSA-UNet: U-Shaped Pansharpening Network with Channel Cross-Concatenation and Spatial-Spectral Attention Mechanism for Hyperspectral Image Super-Resolution.Remote Sensing,15(17),34.
MLA	Z. C. Liu, G. L. Han, H. Yang, P. X. Liu, D. B. Chen, D. X. Liu and A. P. Deng."CCC-SSA-UNet: U-Shaped Pansharpening Network with Channel Cross-Concatenation and Spatial-Spectral Attention Mechanism for Hyperspectral Image Super-Resolution".Remote Sensing 15.17(2023):34.