Unscented particle filter in road extraction from high resoltuion satellite images

Abstract

A typical way to update map is to compare recent satellite images with existing map data, detect new roads and add them as cartographic entities to the road layer. At present image processing and pattern recognition are not robust enough to automate the image interpretation system feasible. For this reason we have to develop image interpretation systems that rely on human guidance. More importantly road maps require final checking by a human due to the legal implementations of error. Our proposed technique is applied to Indian Remote Sensing and IKONOS satellite images using Unscented Particle Filter. Unscented particle filter is used for tracing the median axis of the single road segment. The Extended Kalman Filter is probably the most widely used estimation algorithm for road tracking. However, more than 35 years of experience in the estimation community has shown that is difficult to implement and is difficult to tune. To overcome this limitation, Unscented particle filter is introduced in road tracking which is more accurate, easier to implement, and uses the same order of calculations as linearization. The principles and algorithm of unscented kalman filter and unscented particle filter were also discussed. The core of our system is based on profile matching. Unscented Particle filter traces the road beyond obstacles and tries to find the continuation of the road finding all road branches initializing at the road junction. The completeness and correctness of road tracking from the Indian Remote Sensing and IKONOS images were also compared.