A Method for Automatic Road Extraction of High Resolution SAR Imagery

Abstract

Nowadays automatic road extraction from satellite imageries is considered as one of the most important research trends in the field of remote sensing. This paper presents a method for automatic extraction of road centerlines from synthetic aperture radar (SAR) imagery. During the first step, three features, namely the direction of the least total radiance, the corresponding radiance, and the contrast are extracted to define the road characteristics by the backscatter coefficient of each pixel and its neighboring pixels from the SAR imagery. The fusion of the extracted features is carried out in the next step for detection of the road areas by using a fuzzy inference system. Afterwards, the morphology skeletonization is applied on the road areas to extract the road skeleton. Then some interested seed points are extracted so that they could be used in a snake model, which is employed to connect the seed points in order to form up the road centerlines. The proposed algorithm is tested on different parts of TerraSAR-X images. The experimental results reveal that the proposed method is effective in terms of correctness, completeness, and quality.

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

“…Increasing demands for updating geospatial databases call for the development of more advanced tools to minimize the human interaction in urban feature extraction and identification [1,2]. Road networks not only have vast usage in many applications, such as urban design, navigation, change detection, image registration [3], transportation applications [4,5], personal navigation systems, or vehicle routing [6], but also incorporate a vital component in most GIS systems.…”

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

“…They selected some spectral features and textures parameters and used objectoriented methods to extract roads from the input images. Saati et al [19] proposed an automatic method to extract road centerlines from SAR images. They extracted three features of the road, defining the road features by the backscattering coefficient of each pixel and the adjacent pixels of the SAR images.…”

Detection of Railroad Networks in SAR Images

“…There are many academic studies which extract roads from SAR images [2][3][4][5][6][7][8][9][10][11][12][13] but there are a few studies which determine railroads using SAR's qualities. Hellwich and Streck [14] present a study which searches visibility of linear structures like roads, rivers and railroads in SAR coherence data.…”