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AUTOMATIC ROAD EXTRACTION FROM SATELLITE IMAGES USING EXTENDED KALMAN FILTERING AND EFFICIENT PARTICLE FILTERING

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Author(s): Jenita Subash

Journal: International Journal of Distributed and Parallel Systems
ISSN 2229-3957

Volume: 2;
Issue: 6;
Start page: 135;
Date: 2011;
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Keywords: Satellite images | Extended Kalman Filter | Local Linearization Particle filter | Efficient particle filter

ABSTRACT
Users of geospatial data in government, military, industry, research, and other sectors have need foraccurate display of roads and other terrain information in areas where there are ongoing operations orlocations of interest. Hence, road extraction that is significantly more automated than the employment ofcostly and scarce human resources has become a challenging technical issue for the geospatialcommunity. An automatic road extraction based on Extended Kalman Filtering (EKF) and variablestructured multiple model particle filter (VS-MMPF) from satellite images is addressed. EKF traces themedian axis of a single road segment while VS-MMPF traces all road branches initializing at theintersection. In case of Local Linearization Particle filter (LLPF), a large number of particles are usedand therefore high computational expense is usually required in order to attain certain accuracy androbustness. The basic idea is to reduce the whole sampling space of the multiple model system to the modesubspace by marginalization over the target subspace and choose better importance function for modestate sampling. The core of the system is based on profile matching. During the estimation, new referenceprofiles were generated and stored in the road template memory for future correlation analysis, thuscovering the space of road profiles. .
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