Energy-Efficient Spiking Spectral-Weighting Reconstruction Network for Compressive Hyperspectral Imaging.
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| Title: | Energy-Efficient Spiking Spectral-Weighting Reconstruction Network for Compressive Hyperspectral Imaging. |
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| Authors: | Fang, Zhen1,2 (AUTHOR), Ma, Xu1,2 (AUTHOR) maxu@bit.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1805. 24p. |
| Subjects: | Image reconstruction, Spectral imaging, Artificial neural networks, Feature extraction |
| Abstract: | Highlights: What are the main findings? We overcome several major challenges in adapting spiking neural networks (SNNs) to compressive hyperspectral imaging (CHI) reconstruction tasks and propose the first SNN-based reconstruction network (SSWR-Net) to significantly improve the energy–efficiency ratio in CHI reconstruction. Leveraging the proposed SNN-based spatial–spectral feature extraction modules, customized feature scaling architectures and a novel temporal-wise progressive training method, the proposed network, SSWR-Net, achieves energy-efficient and high-fidelity reconstruction performance on both simulation and real experiments. What are the implications of the main findings? The proposed network, SSWR-Net, overcomes the dependence of existing ANNs on high energy consumption and advanced hardware, making it possible to deploy CHI systems on energy-constrained devices. The principles of this work are general, thus offering great potential to be generalized to various HSI-based classification and fusion tasks, as well as other inverse imaging problems. Recently, artificial neural networks (ANNs) have shown impressive performance in the compressive hyperspectral imaging (CHI) reconstruction task, but the high energy consumption limits their deployment on energy-constrained devices. This paper develops a novel spiking neural network (SNN), termed spiking spectral-weighting reconstruction network (SSWR-Net), to significantly improve the energy–efficiency ratio in CHI reconstruction. Firstly, a spiking spectral-weighting convolution block is proposed to adaptively modulate the spiking signals, enabling the SNN to fit continuous spectral correlation curves. Secondly, a residual feature reuse module with more direct connections is designed to achieve efficient and lightweight spatial–spectral feature extraction. Thirdly, customized feature scaling architectures are introduced to resolve the dimensional mismatch issue and enhance information flow. Finally, we propose a novel temporal-wise progressive training method to optimize the multi-timestep SSWR-Net, which can significantly improve both training efficiency and reconstruction quality. Both simulation and real experiments demonstrate the superiority of the proposed method in both CHI reconstruction performance and energy efficiency. Specifically, SSWR-Net outperforms its ANN-based counterpart by 0.87 dB at a 19.74% energy cost. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We overcome several major challenges in adapting spiking neural networks (SNNs) to compressive hyperspectral imaging (CHI) reconstruction tasks and propose the first SNN-based reconstruction network (SSWR-Net) to significantly improve the energy–efficiency ratio in CHI reconstruction. Leveraging the proposed SNN-based spatial–spectral feature extraction modules, customized feature scaling architectures and a novel temporal-wise progressive training method, the proposed network, SSWR-Net, achieves energy-efficient and high-fidelity reconstruction performance on both simulation and real experiments. What are the implications of the main findings? The proposed network, SSWR-Net, overcomes the dependence of existing ANNs on high energy consumption and advanced hardware, making it possible to deploy CHI systems on energy-constrained devices. The principles of this work are general, thus offering great potential to be generalized to various HSI-based classification and fusion tasks, as well as other inverse imaging problems. Recently, artificial neural networks (ANNs) have shown impressive performance in the compressive hyperspectral imaging (CHI) reconstruction task, but the high energy consumption limits their deployment on energy-constrained devices. This paper develops a novel spiking neural network (SNN), termed spiking spectral-weighting reconstruction network (SSWR-Net), to significantly improve the energy–efficiency ratio in CHI reconstruction. Firstly, a spiking spectral-weighting convolution block is proposed to adaptively modulate the spiking signals, enabling the SNN to fit continuous spectral correlation curves. Secondly, a residual feature reuse module with more direct connections is designed to achieve efficient and lightweight spatial–spectral feature extraction. Thirdly, customized feature scaling architectures are introduced to resolve the dimensional mismatch issue and enhance information flow. Finally, we propose a novel temporal-wise progressive training method to optimize the multi-timestep SSWR-Net, which can significantly improve both training efficiency and reconstruction quality. Both simulation and real experiments demonstrate the superiority of the proposed method in both CHI reconstruction performance and energy efficiency. Specifically, SSWR-Net outperforms its ANN-based counterpart by 0.87 dB at a 19.74% energy cost. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111805 |
