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Users of geospatial data in government, military, industry, research, and other sectors have need for accurate display of roads and other terrain information in areas where there are ongoing operations or locations of interest. Hence, road extraction that is significantly more automated than the employment of costly and scarce human resources has become a challenging technical issue for the geospatial community. An automatic road extraction based on Extended Kalman Filtering (EKF) and variable structured multiple model particle filter (VS-MMPF) from satellite images is addressed. EKF traces the median axis of a single road segment while VS-MMPF traces all road branches initializing at the intersection. In case of Local Linearization Particle filter (LLPF), a large number of particles are used and therefore high computational expense is usually required in order to attain certain accuracy and robustness. The basic idea is to reduce the whole sampling space of the multiple model system to the mode subspace by marginalization over the target subspace and choose better importance function for mode state sampling. The core of the system is based on profile matching. During the estimation, new reference profiles were generated and stored in the road template memory for future correlation analysis, thus covering the space of road profiles. . General Terms Image Processing, Remote sensing

Automatic Road Extraction from Satellite Images Using Extended Kalman Filtering and Efficient Particle Filtering