MTP-Net: road extraction from remote sensing images based on multi-type pooling network

As a popular task in remote sensing, road extraction has been widely concerned and applied by researchers, especially by using deep learning methods. However, many methods ignore the properties of roads in remote sensing images, which have long-range structures or discrete distributions. Therefore, this paper designs a network(MTP-Net) based on strip pooling and multi-scale spatial pooling. This network uses the ResNet50 network as the encoder to achieve feature extraction and ensures the overall connectivity and edge details of roads by the multi-type pooling module including strip pooling and multi-scale spatial pooling. The MTP-Net was tested on the Massachusetts Roads Dataset, the F1-score and IoU(intersection ratio) reached 72.24% and 56.54%, respectively. Compared with the mainstream methods such as UNet and deeplabV3+, the experiment shows that the MTP-Net is superior to the comparison model and has good results in road extraction.