Open AccessEXTRACTING URBAN GROUND OBJECT INFORMATION FROM IMAGES AND LiDAR DATA
Author(s) -
Lina Yi,
Xuesheng Zhao,
Luan Li,
Guifeng Zhang
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b7-411-2016
Subject(s) - segmentation , lidar , object (grammar) , artificial intelligence , computer science , pattern recognition (psychology) , classifier (uml) , object based , feature extraction , data mining , computer vision , remote sensing , geography
To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, this paper uses ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon. Eventually, the multiple classifier combination method is applied to get the outcome of the classification. In order to validate the feasible of this method, this paper selects two experimental regions in Stuttgart and Germany (Region A and B, covers 0.21 km<sup>2</sup> and 1.1 km<sup>2</sup> respectively). The aim of the first experiment on the Region A is to get the optimal segmentation scales and classification features. The overall accuracy of the classification reaches to 93.3 %. The purpose of the experiment on region B is to validate the application-ability of this method for a large area, which is turned out to be reaches 88.4 % overall accuracy. In the end of this paper, the conclusion shows that the proposed method can be performed accurately and efficiently in terms of urban ground information extraction and be of high application value.