AMMNet: An Asymmetric Multi-Modal Network for Remote Sensing Semantic Segmentation

TL;DR

AMMNet introduces an asymmetric multi-modal network architecture that efficiently integrates RGB and DSM data for improved remote sensing semantic segmentation, addressing redundancy and alignment issues.

Contribution

The paper proposes a novel asymmetric architecture with specialized modules for better multi-modal integration and reduced redundancy, advancing the state-of-the-art in remote sensing segmentation.

Findings

Achieves state-of-the-art accuracy on ISPRS Vaihingen and Potsdam datasets.

Reduces computational and memory requirements compared to existing methods.

Effectively handles modality misalignment and enhances feature fusion.

Abstract

Semantic segmentation in remote sensing (RS) has advanced significantly with the incorporation of multi-modal data, particularly the integration of RGB imagery and the Digital Surface Model (DSM), which provides complementary contextual and structural information about the ground object. However, integrating RGB and DSM often faces two major limitations: increased computational complexity due to architectural redundancy, and degraded segmentation performance caused by modality misalignment. These issues undermine the efficiency and robustness of semantic segmentation, particularly in complex urban environments where precise multi-modal integration is essential. To overcome these limitations, we propose Asymmetric Multi-Modal Network (AMMNet), a novel asymmetric architecture that achieves robust and efficient semantic segmentation through three designs tailored for RGB-DSM input pairs. To reduce architectural redundancy, the Asymmetric Dual Encoder (ADE) module assigns representational capacity based on modality-specific characteristics, employing a deeper encoder for RGB imagery to capture rich contextual information and a lightweight encoder for DSM to extract sparse structural features. Besides, to facilitate modality alignment, the Asymmetric Prior Fuser (APF) integrates a modality-aware prior matrix into the fusion process, enabling the generation of structure-aware contextual features. Additionally, the Distribution Alignment (DA) module enhances cross-modal compatibility by aligning feature distributions through divergence minimization. Extensive experiments on the ISPRS Vaihingen and Potsdam datasets demonstrate that AMMNet attains state-of-the-art segmentation accuracy among multi-modal networks while reducing computational and memory requirements.