The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-8
28 Nov 2014
 | 28 Nov 2014

Road Detection from Remote Sensing Images using Impervious Surface Characteristics: Review and Implication

P. P. Singh and R. D. Garg

Keywords: Classification Techniques, HRSI, Impervious Surfaces, Information Extraction, Road Detection

Abstract. The extraction of road network is an emerging area in information extraction from high-resolution satellite images (HRSI). It is also an interesting field that incorporates various tactics to achieve road network. The process of road detection from remote sensing images is quite complex, due to the presence of various noises. These noises could be the vehicles, crossing lines and toll bridges. Few small and large false road segments interrupt the extraction of road segments that happens due to the similar spectral behavior in heterogeneous objects. To achieve a better level of accuracy, numerous factors play their important role, such as spectral data of satellite sensor and the information related to land surface area. Therefore the interpretation varies on processing of images with different heuristic parameters. These parameters have tuned according to the road characteristics of the terrain in satellite images. There are several approaches proposed and implemented to extract the roads from HRSI comprising a single or hybrid method. This kind of hybrid approach has also improved the accuracy of road extraction in comparison to a single approach. Some characteristics related to impervious and non-impervious surfaces are used as salient features that help to improve the extraction of road area only in the correct manner. These characteristics also used to utilize the spatial, spectral and texture features to increase the accuracy of classified results. Therefore, aforesaid characteristics have been utilized in combination of road spectral properties to extract road network only with improved accuracy. This evaluated road network is quite accurate with the help of these defined methodologies.