# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 5 Conclusions  
In this paper, we propose LSANet, a lightweight and efficient network for single-image super resolution in remote sensing scenarios. By introducing a large separable kernel attention mechanism and a compact residual learning structure, LSANet enhances feature representation while maintaining low computational complexity. Extensive experiments on two benchmark remote sensing datasets, UC Merced Land Use and NWPU-RESISC45, demonstrate that LSANet achieves competitive or superior performance compared to several state-of-the-art methods, especially in terms of PSNR, SSIM, and parameter efficiency. The visual results further validate its capability in reconstructing detailed structures such as roads and buildings with fewer artifacts and clearer edges.  
Despite its effectiveness, several limitations remain. First, the current model is optimized for optical RGB imagery; its generalizability to other modalities such as SAR or hyperspectral images remains to be explored. Second, while LSANet supports scale factors up to \(\times\)4, performance under ultra-high scaling or in extremely low-quality inputs may degrade. Additionally, although the model is designed for low computational cost, experiments were conducted on high-end hardware; future work will include validation on edge or embedded devices to further demonstrate its hardware adaptability.  
Looking ahead, we plan to extend LSANet to video super resolution via spatiotemporal modeling and explore hybrid architectures incorporating lightweight Transformer-style modules to enhance global dependency modeling. Furthermore, we aim to evaluate LSANet's effectiveness in operational remote sensing tasks such as land cover classification or disaster monitoring, where improved spatial resolution can yield tangible societal and environmental benefits.  
Conceptualization, T.Y. and X.L.; methodology, T.Y.; data curation, T.Y.; writing--original draft, T.Y.; writing--review and editing, T.Y. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Activation Function** & **Params/K** & **FLOPS/G** & **PSNR** & **SSIM** \\ \hline (l) & Hard Swish & 558 & 13.6 & 27.79 & 0.7635 \\ (m) & – & 556 & 13.4 & 26.25 & 0.7584 \\ (n) & ReLU & 557 & 13.5 & 26.38 & 0.7599 \\ (o) & Sigmoid & 560 & 13.7 & 26.40 & 0.7611 \\ (p) & ELU & 557 & 13.5 & 26.47 & 0.7602 \\ (q) & Tanh & 561 & 13.9 & 27.40 & 0.7622 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different activation functions.  
**Funding:** This work is supported in part by in part by the Special Fund for Training High Level Innovative Talents of Sichuan University of Science and Engineering, under Grant B12402005, and in part the National Natural Science Foundation of China under Grant 42471437.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets in this study are publicly available, and the websites providing access to these datasets are included in the article.  
**Acknowledgments:** We are grateful to the anonymous reviewers for their precious opinions and suggestions, whose professional reviews conspicuously enhanced the quality of this paper. Furthermore, our appreciation goes to the editorial team for their painstaking efforts and guidance throughout the publication process.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.