📄 中文摘要
MeCSAFNet是一种多分支编码器-解码器架构，旨在对多光谱图像中的土地覆盖进行分割。该模型通过双ConvNeXt编码器分别处理可见光和非可见光通道，随后使用各自的解码器重建空间信息。一个专门的融合解码器整合多个尺度的中间特征，将细致的空间线索与高层次的光谱表示相结合。特征融合通过CBAM注意力机制进一步增强，ASAU激活函数则有助于实现稳定和高效的优化。该模型能够处理不同的光谱配置，包括结合RGB和NIR波段的4通道输入以及6通道输入。
多编码器ConvNeXt网络与平滑注意特征融合用于多光谱语义分割
📄 English Summary
Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation
MeCSAFNet is a multi-branch encoder-decoder architecture designed for land cover segmentation in multispectral imagery. The model processes visible and non-visible channels separately using dual ConvNeXt encoders, followed by individual decoders that reconstruct spatial information. A dedicated fusion decoder integrates intermediate features at multiple scales, combining fine spatial cues with high-level spectral representations. Feature fusion is further enhanced with CBAM attention, while the ASAU activation function contributes to stable and efficient optimization. The model is capable of processing different spectral configurations, including a 4-channel input combining RGB and NIR bands, as well as a 6-channel input.