RoGLSNet: An Efficient Global–Local Scene Awareness Network With Rotary Position Embedding for Remote Image Segmentation

Abstract

Accurate segmentation of very high-resolution remote sensing images is vital for downstream tasks. Most semantic segmentation methods fail to fully consider the inherent characteristics of the images, such as intricate backgrounds, significant intraclass variance, and spatial interdependence of geographic object distribution. To address these challenges, we propose an efficient global–local scene awareness network with rotary position embedding (RoGLSNet). Specifically, we introduce the dynamic global filter (DGF) module to adaptively select frequency components, thereby mitigating interference from background noise. For high intraclass variance, the class center aware block (CCAB) performs class-level contextual modeling with spatial information integration. Additionally, the rotary position embedding (RoPE) is incorporated into vanilla attention to indirectly model the positional and distance relationships of geographic target objects. Extensive experimental results on two widely used datasets demonstrate that RoGLSNet outperforms the state-of-the-art (SOTA) segmentation methods. The code is available at https://github.com/bai101315/RoGLSNet