Knowledge-based Classification Method for Urban Area Objects Feature Extraction Based on LIDAR Points

被引：0

作者：

Xu Honggen ^{[1
]}

Li Ting ^{[2
]}

Wu Fang ^{[1
]}

机构：

[1] China Aero Geophys Survey & Remote Sensing Ctr La, Beijing 10083, Peoples R China

[2] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430070, Peoples R China

来源：

MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION IV, PTS 1 AND 2 | 2012年 / 128-129卷

关键词：

LIDAR points; classification; feature extraction; knowledge; -based; fuzzy judgement;

D O I：

10.4028/www.scientific.net/AMM.128-129.1157

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Laser scanning technology can quickly capture a large area of high-precise 3D spatial data, and get the information of buildings, roads, vegetation and other urban objects from raw data. Based on this information general frame of these objects can be modelling. In this paper, an object-based classification method is proposed for urban objects based on LIDAR points: determine the contents of the objects contained in the scene; extract inherent features of different objects; establish objects feature knowledge database; combine and compare objects' features and distribution of LIDAR points; derive a set of rule to express the point cloud classification which can be received by computer through fuzzy judgement. The method has been applied to LIDAR points by LYNX. The experiment results show that the proposed classification method is promising and usable.

引用

页码：1157 / +

页数：2