- #Captureperfect 3.1 image width wider than the paper full
- #Captureperfect 3.1 image width wider than the paper registration
The information of road width is important in several applications. These additional disturbances make separation of the road from its surrounding objects even more difficult. Furthermore, composition of objects which are similar to road pavement (such as roadside rooftops and parking areas) add unwanted noise to the extraction process. The presence of vehicles and lane marks on the road creates an arbitrary spatial and spectral texture that hinders the road extraction process. Because of the variations of materials used in road and road widths that usually occur in an image and because of the presence of disturbance such as nearby buildings, trees and shadows cast by them, extraction of the actual shape of the road is an inherently difficult task. The general characteristics of roads include relatively a slight width difference and changes in its direction, along with relatively uniform color but contrasting with its adjacent area being interlinked to form a road network.
But the high complexity of remote sensing information of the road and other limitations such as lagging-behind of modern computer automation in catching up with the level of the corresponding demand, road extraction has still remain an active research topic in remote sensing. Practical urban applications such as automated road navigation, updating geographic information systems, and geometric correction of urban remote sensing images require accurate and up-to-date road information. Moreover, having an accurate and latest up-to-date road infrastructure database is important for many applications such as topographic mapping and map updating, along with disaster monitoring and safety analysis. Thus, there is a need to represent these imagery data from multiple sources accurately.
#Captureperfect 3.1 image width wider than the paper registration
Nowadays, with the availability of multiple earth observation satellite imagery for the same site/region, the problem of mapping registration between those available imagery and/or even between Volunteered Geographic Information (VGI) and the image arises. Since the past few decades, remote sensing has been an important data source for geographic information. Yet, road extraction is a challenging research topic in the field of remote sensing especially with the advent of high spatial resolution satellite images. The work on extraction of topographic objects like roads from satellite imagery started from late 1970s.
#Captureperfect 3.1 image width wider than the paper full
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Ĭopyright © 2018, Prajowal Manandhar, Prashanth Reddy Marpu and Zeyar Aung License (2021), "Segmentation based traversing-agent approach for road width extraction from satellite images using volunteered geographic information", Applied Computing and Informatics, Vol. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The method consists of image segmentation and traversal of multiple agents along available VGI information. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images.
VGI data is often provided only as vector data represented by lines and not as full extent. We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images.
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