SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data

Lekang Wen; Liang Liao; Jing Xiao; Mi Wang

cs.CVMar 3, 2026

SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data

Authors: Lekang Wen, Liang Liao, Jing Xiao, Mi Wang

Organizations: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University · Hangzhou Institute of Technology, Xidian University · School of Artificial Intelligence, Wuhan University

Abstract

Multimodal semantic segmentation integrates complementary information from diverse sensors for remote sensing Earth observation. However, practical systems often encounter missing modalities due to sensor failures or incomplete coverage, termed Incomplete Multimodal Semantic Segmentation (IMSS). IMSS faces three key challenges: (1) multimodal imbalance, where dominant modalities suppress fragile ones; (2) intra-class variation in scale, shape, and orientation across modalities; and (3) cross-modal heterogeneity with conflicting cues producing inconsistent semantic responses. Existing methods rely on contrastive learning or joint optimization, which risk over-alignment, discarding modality-specific cues or imbalanced training, favoring robust modalities, while largely overlooking intra-class variation and cross-modal heterogeneity. To address these limitations, we propose the Semantic-Guided Modality-Aware (SGMA) framework, which ensures balanced multimodal learning while reducing intra-class variation and reconciling cross-modal inconsistencies through semantic guidance. SGMA introduces two complementary plug-and-play modules: (1) Semantic-Guided Fusion (SGF) module extracts multi-scale, class-wise semantic prototypes that capture consistent categorical representations across modalities, estimates per-modality robustness based on prototype-feature alignment, and performs adaptive fusion weighted by robustness scores to mitigate intra-class variation and cross-modal heterogeneity; (2) Modality-Aware Sampling (MAS) module leverages robustness estimations from SGF to dynamically reweight training samples, prioritizing challenging samples from fragile modalities to address modality imbalance. Extensive experiments across multiple datasets and backbones demonstrate that SGMA consistently outperforms state-of-the-art methods, with particularly significant improvements in fragile modalities.

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Missing-Modality Learning Remote Sensing Image Segmentation Cross-Modal Representation Learning Multimodal Robustness Semantic Segmentation

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