GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916693592244224 |
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| author | Le, Thanh-Dung Ha, Vu Nguyen Nguyen, Ti Ti Eappen, Geoffrey Thiruvasagam, Prabhu Chou, Hong-fu Tran, Duc-Dung Nguyen-Kha, Hung Garces-Socarras, Luis M. Gonzalez-Rios, Jorge L. Merlano-Duncan, Juan Carlos Chatzinotas, Symeon |
| author_facet | Le, Thanh-Dung Ha, Vu Nguyen Nguyen, Ti Ti Eappen, Geoffrey Thiruvasagam, Prabhu Chou, Hong-fu Tran, Duc-Dung Nguyen-Kha, Hung Garces-Socarras, Luis M. Gonzalez-Rios, Jorge L. Merlano-Duncan, Juan Carlos Chatzinotas, Symeon |
| contents | This study introduces ResNet-GLUSE, a lightweight ResNet variant enhanced with Gated Linear Unit-enhanced Squeeze-and-Excitation (GLUSE), an adaptive channel-wise attention mechanism. By integrating dynamic gating into the traditional SE framework, GLUSE improves feature recalibration while maintaining computational efficiency. Experiments on EuroSAT and PatternNet datasets confirm its effectiveness, achieving exceeding \textbf{94\% and 98\% accuracy}, respectively. While \textbf{MobileViT achieves 99\% accuracy}, ResNet-GLUSE offers \textbf{33x fewer parameters, 27x fewer FLOPs, 33x smaller model size (MB), $\approx$6x lower power consumption (W), and $\approx$3x faster inference time (s)}, making it significantly more efficient for onboard satellite deployment. Furthermore, due to its simplicity, ResNet-GLUSE can be easily mimicked for \textbf{neuromorphic computing}, enabling ultra-low power inference at just \textbf{852.30 mW} on Akida Brainchip. This balance between high accuracy and ultra-low resource consumption establishes ResNet-GLUSE as a practical solution for real-time Earth Observation (EO) tasks. Reproducible codes are available in our shared repository. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12484 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Le, Thanh-Dung Ha, Vu Nguyen Nguyen, Ti Ti Eappen, Geoffrey Thiruvasagam, Prabhu Chou, Hong-fu Tran, Duc-Dung Nguyen-Kha, Hung Garces-Socarras, Luis M. Gonzalez-Rios, Jorge L. Merlano-Duncan, Juan Carlos Chatzinotas, Symeon Image and Video Processing This study introduces ResNet-GLUSE, a lightweight ResNet variant enhanced with Gated Linear Unit-enhanced Squeeze-and-Excitation (GLUSE), an adaptive channel-wise attention mechanism. By integrating dynamic gating into the traditional SE framework, GLUSE improves feature recalibration while maintaining computational efficiency. Experiments on EuroSAT and PatternNet datasets confirm its effectiveness, achieving exceeding \textbf{94\% and 98\% accuracy}, respectively. While \textbf{MobileViT achieves 99\% accuracy}, ResNet-GLUSE offers \textbf{33x fewer parameters, 27x fewer FLOPs, 33x smaller model size (MB), $\approx$6x lower power consumption (W), and $\approx$3x faster inference time (s)}, making it significantly more efficient for onboard satellite deployment. Furthermore, due to its simplicity, ResNet-GLUSE can be easily mimicked for \textbf{neuromorphic computing}, enabling ultra-low power inference at just \textbf{852.30 mW} on Akida Brainchip. This balance between high accuracy and ultra-low resource consumption establishes ResNet-GLUSE as a practical solution for real-time Earth Observation (EO) tasks. Reproducible codes are available in our shared repository. |
| title | GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2504.12484 |