Abstract
The up-to-date and information-accurate road database plays a significant role in many applications. Recently, with the improvement in image resolutions and quality, remote sensing images have provided an important data source for road extraction tasks. However, due to the topology variations, spectral diversities, and complex scenarios, it is still challenging to realize fully automated and highly accurate road extractions from remote sensing images. This paper proposes a novel dual-attention capsule U-Net (DA-CapsUNet) for road region extraction by combining the advantageous properties of capsule representations and the powerful features of attention mechanisms. By constructing a capsule U-Net architecture, the DA-CapsUNet can extract and fuse multiscale capsule features to recover a high-resolution and semantically strong feature representation. By designing the multiscale context-augmentation and two types of feature attention modules, the DA-CapsUNet can exploit multiscale contextual properties at a high-resolution perspective and generate an informative and class-specific feature encoding. Quantitative evaluations on a large dataset showed that the DA-CapsUNet provides a competitive road extraction performance with a precision of 0.9523, a recall of 0.9486, and an F-score of 0.9504, respectively. Comparative studies with eight recently developed deep learning methods also confirmed the applicability and superiority or compatibility of the DA-CapsUNet in road extraction tasks.
Highlights
Monitoring and updating the transportation infrastructures is a routine work so as to facilitate the smoothness and guarantee the security of the transportation-related activities
To further improve road extraction accuracy, we design a spatial feature attention (SFA) module over the feature map F to force the network to concentrate on the spatial features tightly associated with the road regions
Last but not least, designed with the SFA module, the proposed DA-CapsUNet was forced to concentrate on the spatial features tightly associated with the road regions and effectively suppress the influences of the background features, thereby providing a powerful class-specific feature encoding
Summary
Monitoring and updating the transportation infrastructures is a routine work so as to facilitate the smoothness and guarantee the security of the transportation-related activities. Road network information updating is usually carried out based on field investigations by well-trained workers or using mobile mapping systems mounted with video cameras/laser scanners and global navigation satellite system (GNSS) antennas. Such means are quite inefficient, labor-intensive, and difficult to rapidly provide for the up-to-date road network database. As it requires a long time and considerable labor to collect the road data covering an entire state or a whole country. The measurement accuracy cannot be well guaranteed due to the incomplete coverage of some road sections or the inoperable conditions of some road segments
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