Road Region Extraction from RSI using K-Mean HFT Segmentation Technique

Abstract

The extraction of roads from digital images is essential for automatic mapping, effective urban planning and updating of GIS databases. The very high spatial resolution images (VHR) taken by space and space probes are the main source of an accurate extraction of the route. The extraction of road networks in remote urban areas of images plays an important role in many urban applications (eg. Road traffic, geometric correction of remote sensing images in cities, updating geographical information, etc.). Because of the complex geometry of buildings and the geometry of sensor detection, it is generally difficult to distinguish the road from its background. In this research work the image segmentation techniques used for image remote sensing are discussed and evaluated. It has been found that there is no perfect method for image segmentation because the result of image segmentation depends on many factors, i.e. Pixel color, consistency, intensity, image similarity, image content and problem area. In this research work, a hybrid method is proposed for the extraction of paths from high resolution images based on the segmentation of the mean and HFT segmentation. The proposed method includes: noise suppression using the Lucy-Richardson algorithm, then a further improvement of the contrast between the initial segmentation of road and non-road pixels using the segmentation means k and finally HFT based segmentation. Simulation will be conducted on remote sensing images in urban, suburban and rural areas to demonstrate the proposed method and compare it with other similar approaches. The results show the validity and superior performance of the proposed extraction method.