## 1 Introduction
With the rapid development of attention mechanism in the field of computer vision, remote sensing image road extraction methods have also begun to incorporate this mechanism, and combining the attention mechanism with CNN can significantly improve the performance and efficiency of the model, as well as enhance the interpretability and adaptability of the model. [PERSON] [11] et al. proposed a novel lane line detection algorithm for unmanned geographic information awareness using the hybrid attention mechanism ResNet and the line anchor classification. The hybrid attention mechanism is added after the convolutional, normalization, and activation layers of the backbone network, respectively, so that the model can pay more attention to the important lane line features in order to improve the relevance and efficiency of feature extraction. [PERSON] [12] et al. proposed a network called CRAE-Net (Cascaded Residual Attention-Enhanced Network), which optimizes feature fusion by integrating dual-attention modules and handles multi-scale features effectively. Thus, it copes with the challenge of recognizing narrow roads and improves the smoothness and connectivity of road boundaries. [PERSON] [13] et al. proposed the Transformer model, which effectively mitigates the long distance dependency challenge in processing inputs and outputs and significantly reduces the consumption of computational resources through its parallel processing nature. [PERSON] [14] et al. proposed the model of Vision Transformer (ViT), which relies on the attention mechanism to ensure that the global information of the image can be effectively utilized at different levels of the network. It solves the problem of detail information loss during road image extraction and improves the accuracy and detail capturing ability of image processing. [PERSON] [15] et al. proposed the Swin Transformer model, a model that significantly improves the quality of road image extraction by reinforcing the connection between contextual semantics. [PERSON] [16] et al. designed a U-shaped dual-resolution road segmentation network which incorporates a feature fusion module. This network framework employs a self-attention mechanism based on CSwin Transformer [17]to construct its encoder part, which effectively obtains the global contextual links between pixels and helps to extract the complete road information. The contributions are as follows:  
1. Based on the MSNet network, for the problem of low utilization of spectral features from HR-RSIs, we propose a refined road extraction algorithm for the guided fusion of spatial and channel features in Multi-spectral images (SC-FMNet);
2. A Multi-spectral branch structure is designed on the basis of fused image branches. The Spatial and Channel reconstruction Convolution (SCConv) module is merged into the two branches, respectively, which cascades the spatial reconstruction unit and the channel reconstruction unit to remove redundant features through reconstruction. 3. The Spatially Adaptive Feature Modulation Mechanism (SAFMM) module is embedded into the decoding structure, which mainly consists of a Spatially Adaptive Feature Modulation (SAFM) unit and a Convolutional Channel Mixer (CCM) unit. The SAFM unit learns the multi-scale features and uses the non-local information to adaptively modulate the features so as to select the most suitable modulation for each pixel position.