Dual-branch Fusion Network Integrating Multispectral Time Series and Hyperspectral Data for Precise Mapping of Liaohe River Delta Wetland, China

Accurate mapping of wetlands is crucial for wetlands conservation, as well as for monitoring and assessing coastal resources and the environment. Multispectral (MSI) satellite image time series have rich temporal evolution characteristics, which can reveal dynamic changes in surface cover and environmental conditions. However, due to the limited number of bands, the ability to express the difference of ground features is limited, resulting in an inability to capture surface objects’ changes in the finer spectral range. Therefore, this paper proposed a dual-branch spatial-temporal spectral feature fusion network (Fusion-Former), which combined MSI time series data with hyperspectral (HSI) data to achieve accurate mapping of wetlands in Liaohe River Delta, China in 2022. Fusion-Former achieved an overall accuracy (OA) of 96.36% in the Liaohe River Delta wetland, significantly outperforming all benchmark methods. Experimental results demonstrate that utilizing the temporal phenological information from multi-temporal MSI and the fine-grained spatial-spectral features from HSI can effectively resolve the misclassification between spectrally similar vegetation and water bodies. Furthermore, a continuous improvement in accuracy was observed as the length of the input time series increased, underscoring the critical role of temporal information. Therefore, by integrating these complementary information sources, the proposed method enables the generation of accurate wetland maps to support decision-makers in formulating more precise conservation and management strategies.