Road extraction from high-resolution remote sensing images based on the combination of K-means and SVM

Extracting road information from high-resolution remote sensing images is an important way to obtain basic data of geographic information. In this paper, firstly, the shortcomings of K-means and SVM are analyzed, and then the road information is extracted by the algorithm combining K-means and SVM. The experimental results show that the combined algorithm has higher accuracy and lower missing error than the single algorithm. The experimental results can provide some technical support for future road information extraction.