Multiscale Residual Convolution Neural Network and Sector Descriptor-Based Road Detection Method

Abstract

Road detection is a focus of research in the field of remote sensing image analysis. This task is normally difficult due to the complexity of the data, which are heterogeneous in appearance with large intra-class and lower inter-class variations that frequently lead to large numbers of gaps and errors in road extraction. In this paper, a novel road detection method is proposed that combines a multiscale deep residual convolution neural network (MDRCNN) with postprocessing. The MDRCNN is used to obtain road areas more accurately and quickly. Multiscale convolution, which provides greater accuracy, is used to acquire the hierarchical features of different dimensions. The residual connections and global average pooling are introduced to improve the efficiency of the network in the process of backpropagation and forward propagation, respectively. In the postprocessing stage, the centerline of the road can be obtained based on the road area. Geometric constraints and mathematical morphological refinement as well as leaf-to-leaf connection are used to obtain the road line. Rectangular buffer analysis and a sector descriptor tracking connection are subsequently used to improve the integrity and accuracy of the road. We experimented on two datasets with different resolutions and different scenes. Compared with other neural network methods, our method is better at connecting road fractures and eliminating errors.

Keywords

• Geometric constraints
• global average pooling
• mathematical morphological refinement
• multiscale convolution
• multiscale deep residual convolution neural network
• remote sensing