A novel FMH model for road extraction from high-resolution remote sensing images in urban areas

With the rapid development of remote sensing satellites and sensors, the higher resolution of remote sensing images can be collected. Comparing with roads of countryside, it is more difficult to extract roads of urban areas from remote sensing images due to various interference factors including buildings and vehicles. There are regional characteristics with specific width and gray levels of the urban road in high-resolution images. In this article, a novel model named FMH is proposed to extract the main road information from the high-resolution remote sensing image in urban areas. After pre-processing of the image, fuzzy c-means algorithm is applied to binary processing, and thus the image is divided into the road part and the non-road part. Then mathematical morphology is used to eliminate more non-road regions. Next the local Hough transform is applied to extracting the road regions. Finally morphological operations are used to modify and refine shapes of roads. This model is tested on the panchromatic image with the resolution of 2m collected by Gaofen-1 satellite, which shows effective and excellent results.