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 an image interpretation system 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 IRS and IKONOS images using Unscented Kalman Filter(UKF) . UKF is used for tracing the median axis of the single road segment. The Extended Kalman Filter (EKF) 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,UKF 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 EKF and UKF were also discussed. The core of our system is based on profile matching.UKF traces the roadbeyond 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 IRS and IKONOS images were also compared.
Subash, Jenita and K, Madhan Kumar
"Road Tracking from High resolution IRS And IKONOS Images Using Unscented Kalman Filtering,"
International Journal of Electronics Signals and Systems: Vol. 1
, Article 3.
Available at: https://www.interscience.in/ijess/vol1/iss1/3