LANet: Lightweight Attention-Mamba Network for Semantic Segmentation of High-Resolution Remote Sensing Images

作者：Qingxiang Meng, Chengkun Zhang, Bin Yao, Xiandong Sun, Xuyang Hu, Lu Wang, Xiaodan Zhang · 发表于：IEEE Geoscience and Remote Sensing Letters · 年份：2026 · DOI：10.1109/lgrs.2026.3676266 · 研究领域：Remote-Sensing Image Classification、Advanced Neural Network Applications、Automated Road and Building Extraction

Balancing long-range dependencies and efficiency is crucial for remote sensing segmentation. While State Space Models (SSMs) offer linear complexity, they often struggle with local details. To address this, we propose the Lightweight Attention-Mamba Network (LANet). It incorporates an Attention-Guided State Space Module (AG-SSM) to transform indiscriminate scanning into selective refinement of salient features, and a Difference-Aware Gated Fusion (DAGF) module for effective multi-scale integration. LANet achieves state-of-the-art performance on ISPRS and LoveDA datasets. Notably, it attains 51.24% mIoU on LoveDA with only 8.23 M parameters, significantly outperforming UNetFormer (11.72 M) and RS3Mamba (43.33 M) in efficiency.