Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation
It addresses the need for efficient and accurate semantic segmentation of multi-source remote sensing images, which is important for Earth observation applications.
The paper proposes ARG-Mamba, a state space model for optical-elevation image segmentation that improves multi-scale context modeling and cross-modal fusion, achieving state-of-the-art results on ISPRS Vaihingen and Potsdam datasets with favorable computational efficiency.
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}.