A COMPARISON OF BAYESIAN AND EVIDENCE-BASED FUSION METHODS FOR AUTOMATED BUILDING DETECTION IN AERIAL DATA

摘要

Automated approaches to building detection are of great importance in a number of different applications including map updating and monitoring of informal settlements. With the availability of multi-source aerial data in recent years, data fusion approaches to automated building detection have become more popular. In this paper, two data fusion methods, namely Bayesian and Dempster-Shafer, are evaluated for the detection of buildings in aerial image and laser range data, and their performances are compared. The results indicate that the Bayesian maximum likelihood method yields a higher detection rate, while the Dempster-Shafer method results in a lower false-positive rate. A comparison of the results in pixel level and object level reveals that both methods perform slightly better in object level.