A Road Extraction Algorithm for the Guided Fusion of Spatial and Channel Features from Multi-Spectral Images

In the road extraction task, for the problem of low utilization of spectral features in high-resolution remote sensing images, we propose a Multi-spectral image-guided fusion of Spatial and Channel Features for road extraction algorithm (SC-FMNet). The method is designed with a two-branch input network structure including Multi-spectral image and fused image branches. Based on the original MSNet model, the Spatial and Channel Reconstruction Convolution (SCConv) module is introduced in the coding part in each of the two branches. In addition, a Spatially Adaptive Feature Modulation Mechanism (SAFMM) module is introduced into the decoding structure. The experimental results in the GF2-FC and CHN6-CUG road datasets show that the method can better extract the road information and improve the accuracy of road segmentation, which verify the effectiveness of SC-FMNet.