Abstract: High resolution remote sensing image segmentation has a good application prospect in the military and civil fields, but due to the complex background conditions and the obstruction of interferences, the existing algorithms can’t extract road details from remote sensing images. Based on the improved U-Net network model, MDAU-Net (multi dimension attention U-Net) network structure model was proposed. The U-Net network structure was deepened to a seven-layer structure to improve the ability of fine segmentation of roads. A new multidimensional attention module,which was called MD-MECA (multi dimension modified efficient channel attention), was proposed to optimize the feature transfer in the coding part. DropBlock and Batch Normalization were used to resolve the overfitting during network training. The experimental results show that the improved algorithm can effectively improve the road extraction effect, and the accuracy rate on the test set reaches 97.04%.

Key words: remote sensing image, road extraction, U-Net network, multi-dimension attention, characteristics of the transfer