AGF-Net: adaptive global feature fusion network for road extraction from remote-sensing images

Abstract

Abstract Road extraction from remote-sensing images is of great significance for vehicle navigation and emergency insurance. However, the road information extracted in the remote-sensing image is discontinuous because the road in the image is often obscured by the shadows of trees or buildings. Moreover, due to the scale difference of roads in remote-sensing images, it remains a computational challenge to extract small-size roads from remote-sensing images. To address those problems, we propose a road extraction method based on adaptive global feature fusion (AGF-Net). First, a dilated convolution strip attention (DCSA) module is designed from the encoder–decoder structure. It consists of the dilated convolution and the strip attention module, which adaptively emphasizes relevant features in vertical and horizontal directions. Then, multiple global feature fusion modules (GFFM) in the skip connection are designed to supplement the decoder with road detail features, and we design a multi-scale strip convolution module (MSCM) to implement the GFFM module to obtain multi-scale road information. We compare AGF-Net to state-of-the-art methods and report their performance using standard evaluation metrics, including Intersection over Union (IoU), F1-score, precision, and recall. Our proposed AGF-Net achieves higher accuracy compared to other existing methods on the Massachusetts Road Dataset, DeepGlobe Road Dataset, CHN6-CUG Road Dataset, and BJRoad Dataset. The IoU obtained on these datasets are 0.679, 0.673, 0.567, and 0.637, respectively.

Keywords

• Deep learning
• Road extraction
• Remote-sensing images
• Multi-scale feature
• Attention mechanism