文章基本信息

标题：A Comparison of Bayesian and Evidence-Based Fusion Methods for Automated Building Detection in Aerial Data
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作者：K. Khoshelham ; S. Nedkov ; C. Nardinocchi 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：1183-1188
出版社：Copernicus Publications
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
关键词：Fusion; Building detection; Automation; Aerial image; Laser scanning