Tracking Road Centerlines from Remotely Sensed Imagery Using Mean Shift and Kalman Filtering

Abstract

Road tracking based on template matching is one class of practical methods of road extraction. However, the conventional methods of template matching mainly utilize correlation coefficient as the similarity measure. As a result, these algorithms are sensitive to occlusions caused by vehicles and trees and are unsuitable for road extraction from high-resolution remotely sensed imagery. To address this problem, this paper designs a road center matching algorithm based on mean shift utilizing a robust similarity measure, which overcomes the sensitivity of correlation coefficient matching to occlusions; then Kalman filter is utilized to track road centerlines from high-resolution remotely sensed imagery. Experimental results demonstrate that the proposed method can extract road centerlines from high-resolution remotely sensed imagery accurately and is robust to occlusions caused by vehicles and trees