用轴向关系引导融合，提升光学与高程图像分割精度
Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation
- 引入多尺度状态空间模块，线性复杂度捕捉局部细节与全局上下文
- 设计轴向引导融合模块，沿水平垂直方向建模跨模态相关性
- 在Vaihingen和Potsdam数据集上超越现有方法，兼顾性能与效率
多源遥感图像语义分割是地球观测应用的基础任务。现有方法常因多尺度上下文建模不足及跨模态特征融合不佳，在复杂高分辨率场景中表现受限。为此，本文提出基于状态空间模型的光学-高程遥感图像分割框架：轴向关系引导融合Mamba（ARG-Mamba）。具体而言，引入多尺度状态空间模块（MS-SSM），以线性计算复杂度同时捕获细粒度局部细节与全局上下文依赖；此外，设计轴向关系引导融合模块（ARGFM），沿水平与垂直轴显式建模跨模态全局相关性，实现光学与高程模态间高效特征融合。在ISPRS Vaihingen与Potsdam数据集上的大量实验表明，所提ARG-Mamba持续优于当前最优方法，且保持良好计算效率。代码将公开于\url{https://github.com/oucailab/ARG-Mamba}。
原文摘要 · Abstract (English)
Semantic segmentation of multi-source remote sensing images is a fundamental task for Earth observation applications. Existing methods often struggle with insufficient multi-scale context modeling and suboptimal cross-modal feature fusion, limiting their performance in complex high-resolution scenes. To this end, we propose Axial-Relation Guided Fusion Mamba (ARG-Mamba), a state space model-based framework for optical-elevation remote sensing image segmentation. Specifically, we introduce a Multi-Scale State Space Module to capture both fine-grained local details and global contextual dependencies with linear computational complexity. Moreover, an Axial-Relation Guided Fusion Module is designed to explicitly model global cross-modal correlations along horizontal and vertical axes, enabling efficient feature fusion between optical and elevation modalities. Extensive experiments conducted on the ISPRS Vaihingen and Potsdam datasets demonstrate that our ARG-Mamba consistently outperforms state-of-the-art methods while maintaining favorable computational efficiency. The code will be made publicly available at \url{https://github.com/oucailab/ARG-Mamba}.
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