In recent years, optical remote sensing image salient object detection (ORSI-SOD) has made substantial progress. Nevertheless, it remains an open-ended research area with complex challenges. Most existing ORSI-SOD methods, aiming for high-performance detection, demand large-scale parameters and high computational costs. This significantly restricts their application on resource-constrained devices, which have limited computing power and memory capacity. To tackle this issue, we propose a lightweight and highly efficient ORSI-SOD network, termed RAMENet. With only 5.18 M parameters and 8.72 G FLOPs, RAMENet can achieve competitive detection accuracy compared to state-of-the-art (SOTA) methods. Specifically, we devise a dynamic region-aware block (DRB) that can be nested within the encoder to realize plug-and-play functionality. This enables the network to learn ORSI domain-specific feature representations, thus more effectively locating salient object regions. Furthermore, we present a novel multipath-enhanced M-shaped decoder (MED), which integrates both bottom-up and top-down paradigms. Comprising two feature extraction sub-branches and a central feature refinement branch, this architecture achieves multigranularity feature aggregation via cross-level feature interaction. Consequently, it significantly improves the detailed representation capability while maintaining the integrity of the object structure. Extensive experimental results indicate that the RAMENet outperforms five SOTA lightweight methods in terms of S-alpha , F-beta (mean), and MAE on EORSSD and ORSSD datasets, with improvement reaching 0.68%, 0.92%, 0.13%, 0.60%, 1.13%, and 0.07%, respectively. The code and results are available at https://github.com/hjy0518/RAMENet/
