Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation

TL;DR

The paper introduces ARG-Mamba, a novel state space model framework that enhances multi-source remote sensing image segmentation by effectively modeling multi-scale context and cross-modal feature fusion.

Contribution

It proposes a Multi-Scale State Space Module and an Axial-Relation Guided Fusion Module for improved optical-elevation image segmentation.

Findings

Outperforms existing methods on ISPRS Vaihingen and Potsdam datasets.

Achieves better segmentation accuracy with efficient computation.

Demonstrates robustness in complex high-resolution scenes.

Abstract

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}.