An Approach for Road Material Identification By Dual-Stage Convolutional Networks

摘要

The automatic extraction of road network information from satellite images is a meaningful and challenging task. Particularly, the analysis of road surface materials is very important during transport construction and maintenance. This paper proposes a method to extract road area and identify its corresponding materials. The approach is based on two different convolutional neural network structures. Firstly, we use encoder-decoder symmetric network structure to extract the candidate road area. Then the former outputs is processed by atrous convolutional network with very deep layers, in order to classify the covered substances through their representative spectral features. We also utilize the physical characteristics of road network to design morphology approach to enhance the completeness and formation of the road network. Experiential results on various satellite images show that the method can yields better accuracy and adaptability than other convolutional network based methods.