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.
Highlights
The results demonstrate that our proposed model outperforms state-of-the-art frameworks in terms of accuracy, precision, recall, and F-score
Road segmentation plays an important role in many cases applications such as intelligent transport systems and town planning
In this research the image segmentation techniques used for image remote sensing are discussed and evaluated
Summary
Remote images have many uses in agriculture, geology, forestry, biodiversity conservation, land use planning, education, reconnaissance and war. Remote sensing applications (RS) mainly include: urban remote sensing, basic geographic mapping, environmental monitoring and evaluation, precision agriculture and public information services, etc. The purpose of the RS applications is to obtain information and identify the objectives involved in order to complete the understanding of the image. Satellite imagery is becoming increasingly important for the community due to the compilation of maps, population analysis, efficient precision agriculture and the task of autonomous driving, since satellite imagery contains more structured and uniform data than conventional imagery [2]. Understanding satellite imagery, including how to extract roads, identify buildings and identify land cover types, is essential for sustainable development, agriculture, forestry, urban planning and climate change research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
