C²Net: Road Extraction via Context Perception and Cross Spatial-Scale Feature Interaction

Road extraction from remote sensing images (RSIs) holds significant application value in various aspects of daily scenarios. However, it is still challenging to extract high-quality road results from RSIs due to the interference of objects sharing similar structures with roads in the background and the occlusion caused by surroundings. To alleviate these problems, a road extraction network based on the global-local Context perception and Cross spatial-scale feature interaction is proposed (C²Net). First, a global-local context perception module (GLCPM) is incorporated to capture the overall topology features of the road, which aims to improve the ability of the model to discriminate between roads and similar objects. Then, the cross spatial-scale feature interaction module is designed in the skip connection to effectively aggregate full-scale features without loss of feature information, which can provide rich and accurate road structural features for the decoder. Experiments conducted on public road datasets demonstrate that C² Net outperforms existing methods in terms of comprehensive metrics such as intersection over union (IoU) and the F1-score. The results indicate that C² Net can produce road results with superior connectivity and quality. The source code will be publicly available at https://github.com/CVer-Yang/CCNet.