GMUG: A U-Net Processed GRU Optimized Graph-Mamba Network for Wetland Classification

doi:10.1111/1752-1688.70132

GMUG: A U-Net Processed GRU Optimized Graph-Mamba Network for Wetland Classification

2026-06-16

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DOI：10.1111/1752-1688.70132

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Wetland classification has always been a difficult task for researchers. Its land-cover classes share similar spectral signatures and have complex spatial patterns. In this paper, we developed Graph-Mamba with U-Net and GRU (GMUG) for joint HSI and LiDAR classification. The model uses a U-Net model to capture spatial structure, Mamba and GRU modules to refine features, and a graph-based classification stage to contain spatial relationships. GMUG was evaluated on three benchmark HSI/LiDAR datasets, MUUFL, Houston, and Trento, and compared with DAHGMN, MHST, HLMamba, and CMFAEN. Its clearest advantage appeared on MUUFL, where stronger class imbalance and class confusion have challenged all other models. The model also performed well on the Houston and Trento datasets. The ablation experiments showed that both U-Net and GRU improved the full model. When applied to the Louisiana wetland HSI/LiDAR dataset, GMUG classified most of the classes very well, although confusion persisted between classes with very similar signatures, such as Riverine and Lake. In conclusion, GMUG is a useful tool for complex wetland scenes where similar classes are difficult to separate.

Keywords:

GMUG

GNN

Graph-Mamba wetland classification

GRU

Mamba

U-Net

GMUG: A U-Net Processed GRU Optimized Graph-Mamba Network for Wetland Classification

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