In order to address the challenges posed by cloud and vegetation occlusion, as well as low resolution in satellite remote sensing images, which often result in low extraction accuracy, a road extraction method of DLinkNet-Road based on eight-directional stripe pooling was proposed for road extraction in satellite remote sensing images. Firstly, by combining the multi-directional strip features of roads and the advantages of strip pooling for extracting slender targets, an eight directional strip pooling road extraction module was constructed, effectively establishing long-distance multi-directional dependencies of road pixels. Secondly, considering the problems of road breakage caused by occlusion and loss of road contour details caused by convolution pooling operations, a road feature weighted compensation module was designed and a weighted feature fusion structure was constructed to effectively fuse road information from multiple scale features. In experiments on two road datasets, DeepGlobe and Massachusetts, the proposed method achieves an intersection over union (IoU) ratio of 67.42% and 66.38%, respectively, which was 3.89% and 3.17% higher than the baseline model. The experimental results show that the model proposed can improve the disconnection phenomenon in road extraction and ensure the integrity of the road extraction results.