Richer U-Net: Learning More Details for Road Detection in Remote Sensing Images

Road detection in remote sensing images has been an important research topic in the past few decades. However, with complex backgrounds and occlusion of vehicles and trees, it is difficult for most road detection methods to obtain complete and accurate results. There will be a large number of error and omission detections in such complex scenes due to the poor utilization of detailed information. Therefore, in this article, we propose a novel road detection method called Richer U-Net, which alleviates this problem by designing two detail enhancement strategies. First, considering that convolution operation will cause the loss of detailed information in the feature map, an enhanced detail recovery structure (EDRS) is introduced to make full use of those lost information. It combines the output of each convolutional layer at the same level for the detail recovery of decoding network, leading to more accurate segmentation results. Second, an edge-focused loss function is proposed to guide the network to pay more attention to the road edge area. By adding an enhancement factor, the pixels closer to edge will contribute more loss. The corresponding experiments are conducted on two public datasets, and it can be shown that our method effectively improves final detection results.