基于轴向关系引导的融合状态空间模型用于光学-海拔感测图像分割
Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation
AI总结：
本文提出了一种基于状态空间模型的框架，用于光学-海拔遥感图像分割，通过引入多尺度状态空间模块和轴向关系引导融合模块，有效提升了多源遥感图像语义分割的性能和计算效率。
摘要：
多源遥感图像的语义分割是地球观测应用中的基本任务。现有方法在多尺度上下文建模不足和跨模态特征融合不优方面存在困难，限制了其在复杂高分辨率场景中的性能。为此，我们提出轴向关系引导融合Mamba（ARG-Mamba），一种基于状态空间模型的框架，用于光学-海拔遥感图像分割。具体而言，我们引入了多尺度状态空间模块，以线性计算复杂度捕获细粒度局部细节和全局上下文依赖。此外，设计了轴向关系引导融合模块，以显式建模水平和垂直轴上的全局跨模态相关性，从而在光学和海拔模态之间实现高效的特征融合。在ISPRS Vaihingen和Potsdam数据集上进行的广泛实验表明，ARG-Mamba在保持有利的计算效率的同时，始终优于最先进的方法。代码将在https://github.com/oucailab/ARG-Mamba上公开发布。
英文摘要：
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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