Geometry and texture noise make it difficult to accurately describe road image rules, which leads to the low degree of automation of traditional template matching algorithms based on internal texture homogenization. We propose a semi-automatic road extraction method based on multiple descriptors to improve the degree of automation while ensuring the accuracy of road extraction. This method aims to address the problems of incomplete road image geometric information and poor homogeneity of internal road texture. The MLSOH (multi-scale line segment orientation histogram) model and sector descriptor are established. Road points are tracked by interpolation and extension, and post-processing is used to fit the tracking points and extract the road routes. In this paper, high-resolution remote sensing images of different types, different resolutions and different scenes are selected, and the roads exhibit curvatures, vehicle and shadow occlusions, roundabouts and variational features. Experiments show that for roads with a certain width, completeness and correctness of the method are more than 98%. Additionally, compared with other algorithms, the interactive human intervention of this method is reduced by more than 2/3.

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.