Fe-LinkNet: Enhanced D-LinkNet with Attention and Dense Connection for Road Extraction in High-Resolution Remote Sensing Images

Extracting roads from high-resolution remote sensing images automatically is more efficient than field acquisition and manual annotation by experts. However, most road extraction methods based on deep-learning have problems of poor connectivity, due to occlusion of buildings and trees or confusion of backgrounds that have similar texture. In this paper, we proposed a novel feature-enhanced D-LinkNet (FE-LinkNet) to deal with the problem that road information is vulnerable to loss. Firstly, we introduced the idea of dense connection in the down-sampling stage to provide enhanced information for subsequent modules. Then we redesigned the D-Block to DP-Block in another cascade way to extract densely multi-scale contexts for road extraction. Finally, we adopted the self-attention mechanism in the up-sampling stage to learn long-distance pixel dependence to improve the connectivity of roads. Experimental results on CHN6-CUG Road Dataset prove that our FE-LinkNet performs better in accuracy and connectivity than D-LinkNet.