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Road Extraction from Satellite Images Using Unscented Kalman Filter and Gauss-Hermite Kalman Filter
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Abstract
To many geographic systems (GIS) application scheme such as urban planning and navigation, updating road network database is critical problem. Rapidly changing urban environments accelerate the need for frequent updates or revisions of road network databases. With the advent of high-resolution satellite images, there has been a resurgence of research interest in road extraction techniques. However, due to the extreme complexity of an urban scene, automatic road network extraction continues to be challenging research topic. In this paper, we have proposed a road map extraction system with two efficient filters using satellite images. Here, Unscented Kalman filter (UKF) is used in combination with Gauss-Hermite Kalman Filter (GHKF) to trace and identify various connected road paths and to avoid obstacles under diverse conditions. Unscented Kalman filter (UKF) component is responsible for tracing axis coordinates of a road region until it comes to a severe obstacle or an intersection. Then, the Gauss-Hermite Kalman Filter (GHKF) module takes the control of the road extraction process and regains track of the road or possibly road branches on the other side of a road junction or obstacle. From the results, we ensure that the proposed road extraction technique outperformed the existing approach by achieving the accuracy of 98.452% in cluster 10.
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References
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