LULC database updating from VHR images and LIDAR data using evidence theory

被引：0

作者：

Rodriguez-Cuenca, B. ^{[1
]}

Alonso, M. C. ^{[1
]}

Tames-Noriega, A. ^{[1
]}

机构：

[1] Univ Alcala de Henares, Dept Phys & Math, Madrid, Spain

来源：

PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY | 2015年 / 89卷

关键词：

Evidence theory; VHR imagery; LIDAR; pattern recognition; segmentation; CLASSIFICATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Urban growth and the development of urban plans make cities grow and substantially alter, in relatively short time periods, their land covers and land uses (LULC). To take control of this urban growth, it is important to create and update the LULC database. In this work, a method to automatically extract land covers from satellite VHR imagery and LIDAR data is presented. This method is based on the Dempster-Shafer evidence theory. The efficiency of this method is tested in three test sites in the Spanish city of Gijon. The provided results are compared with the SIOSE database in order to determine changes in the LULC.

引用

页码：987 / 993

页数：7

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