轻量级注意力模块让小模型在卫星上高效高精度识别地球影像
GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification
- 在SE注意力中引入动态门控，实现自适应通道特征重校准
- 在EuroSAT和PatternNet上分别达94%和98%准确率，参数少33倍
- 适合边缘部署，可低至852.3mW运行，适合卫星与类脑计算
本研究提出ResNet-GLUSE，一种基于门控线性单元增强型挤压-激励（GLUSE）的轻量级残差网络变体，该机制通过在传统SE框架中引入动态门控，提升特征重校准能力同时保持计算高效。在EuroSAT和PatternNet数据集上的实验验证其有效性，分别达到94%和98%的准确率。尽管MobileViT可实现99%准确率，但ResNet-GLUSE参数量减少33倍、浮点运算减少27倍、模型大小缩小33倍、功耗降低约6倍、推理速度提升约3倍，显著适用于星载部署。此外，由于结构简单，该模型可轻松模拟用于类脑计算，在Akida Brainchip上实现仅852.30 mW的超低功耗推理。这一高精度与极低资源消耗的平衡，使其成为实时地球观测任务的理想方案。可复现代码已开源。
原文摘要 · Abstract (English)
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.
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