Multiscale road centerlines extraction from high-resolution aerial imagery

Abstract

Accurate road extraction from high-resolution aerial imagery has many applications such as urban planning and vehicle navigation system. The common road extraction methods are based on classification algorithm, which needs to design robust handcrafted features for road. However, designing such features is difficult. For the road centerlines extraction problem, the existing algorithms have some limitations, such as spurs, time consuming. To address the above issues to some extent, we introduce the feature learning based on deep learning to extract robust features automatically, and present a method to extract road centerlines based on multiscale Gabor filters and multiple directional non-maximum suppression. The proposed algorithm consists of the following four steps. Firstly, the aerial imagery is classified by a pixel-wise classifier based on convolutional neural network (CNN). Specifically, CNN is used to learn features from raw data automatically, especially the structural features. Then, edge-preserving filtering is conducted on the resulting classification map, with the original imagery serving as the guidance image. It is exploited to preserve the edges and the details of the road. After that, we do some post-processing based on shape features to extract more reliable roads. Finally, multiscale Gabor filters and multiple directional non-maximum suppression are integrated to get a complete and accurate road network. Experimental results show that the proposed method can achieve comparable or higher quantitative results, as well as more satisfactory visual performance.