Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35-12.8% and 2.41-9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.

Automated summary

This paper proposes an effective method to improve road extraction accuracy and reconstruct broken road lines caused by ground occlusion. An attention mechanism-based convolution neural network is established for feature enhancement, and a heuristic method based on connected domain analysis is proposed for road reconstruction. Experimental results show the effectiveness of the method in road extraction.