Semantic Segmentation of Multispectral Remote Sensing Imagery for Coastal Wetlands with SegFormer

Pixel-level semantic segmentation plays an essential role in coastal wetland monitoring using multispectral remote sensing imagery. However, accurate mapping remains challenging due to spectral confusion among heterogeneous land-cover types, fragmented spatial structures, and pronounced class imbalance. Based on the situation, we used the original SegFormer as the basic framework and developed an improved framework to better suit the characteristics of coastal wetland scenes. Prior to the encoder, we introduced a Spectral-Aware Embedding (SAE) module to strengthen inter-band feature representation through spectral projection and adaptive channel weighting. In the decoder, we constructed a Wetland Boundary-Refined Decoder (WBRD), utilizing a dual-path refinement strategy to capture fine-scale textures and a multi-scale boundary attention mechanism to enhance the delineation of irregular boundaries. Additionally, we incorporated a Wetland Imbalance Loss (WIL) during training to moderate the influence of dominant classes. In this article, we evaluated our framework on the Yan14 dataset. The results showcased the framework’s effectiveness, improving segmentation accuracy and boundary fidelity, particularly for rare and narrow wetland categories, while maintaining reasonable computational efficiency.