Brightness Transformation and CNN-MRF Model for Road Network Extraction using RSI

Abstract

For effective urban planning and GIS database, it is necessary to extract effectively the network of road from remote sensing images. The very high spatial resolution images (VHR) taken by space and space probes are the main source of an accurate extraction of the route. Manual techniques disappear because they take time and are expensive. As a result, the much more automated route extraction method has become a research tool in the processing of remote sensing information. 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 paper, a hybrid method is proposed for the extraction of paths from high resolution images based on the segmentation using sigmoid CNN-MRF model. The proposed method includes noise removal and enhancement using brightness transformation function then segmentation of road and non-road pixels using CNN and edges are joined using CNN model also. And lastly the markov random field is used connecting edges with similar end points. 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 better performance of proposed road network extraction method as compared to existing technique.