Abstract
Hyperspectral images (HSI) are characterized by high spectral resolution but low spatial resolution, which is limited by the capabilities of imaging sensors. Due to the high-dimensional nature of HSIs and the correlation between spectra, existing super-resolution methods for HSIs suffer from excessive number of parameters and insufficient complementary information between spectra. This paper proposes a lightweight hyperspectral image super-resolution method based on multiple attention mechanisms, which simplifies each part of the network into a few simple yet effective network modules. Including a large kernel pixel attention (LKPA) network to extract shallow features from HSI. Efficient channel attention (ECA) is utilized to capture similar features across multiple channels. An Efficient Transformation Layer (ETL) network to extract deep features. A contextual incremental fusion (CIF) to exploring spectral feature information. Through large number of verifications on the two general hyperspectral datasets, the excellent experimental results achieved by our proposed method with very few model parameters demonstrate its superiority.
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Acknowledgements
this research was funded by the National Key R&D Program of China (grant number 2020YFA0713503), the Science and Technology Project of Hunan Provincial Natural Resources Department (grant number 2022JJ30561), the Scientific Research Project of Natural Resources in Hunan Province (grant number 2022-15), the Science and Technology Project of Hunan Provincial Natural Resources Department (grant number 2023JJ30582), and supported by Postgraduate Scientific Research Innovation Project of Hunan Province (grant number QL20220161) and Postgraduate Scientific Research Innovation Project of Xiangtan University(grant number XDCX2022L024).
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Bu, L., Dai, D., Zhang, Z., Xie, X., Deng, M. (2023). A Lightweight Hyperspectral Image Super-Resolution Method Based on Multiple Attention Mechanisms. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_53
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