DocumentCode :
143666
Title :
Orientation estimation of building using DSM and optical images based on Zernike moments
Author :
Shu Tian ; Ye Zhang ; Yiming Yan ; Yanfeng Gu ; Nan Xu
Author_Institution :
Dept. of Inf. Eng., Harbin Inst. of Technol., Harbin, China
fYear :
2014
fDate :
13-18 July 2014
Firstpage :
3180
Lastpage :
3183
Abstract :
Digital Surface Models (DSMs) generated from airborne laser-scanning or stereo satellite images provide a very useful source of information for building orientation estimation. However, due to the high complexity of the building structure, it is hardly to get the details of the building boundary information. In this paper, a new orientation estimation method is proposed, which merge the edge information extracted from optical images into DSM, and take advantage of the ZM phase information in the orientation estimation process. The classical way of extracting Zernike features only takes into account the magnitude of the moments and loses the phase information. The novelty of our approach is to take advantage of the phase information of Zernike moments to capture these variabilities in a way that makes it robust in the context of rotation angle estimation.
Keywords :
edge detection; feature extraction; geophysical image processing; geophysical techniques; remote sensing by laser beam; stereo image processing; ZM phase information; Zernike features extraction; Zernike moments; airborne laser-scanning; building orientation estimation; building structure; digital surface models; edge information; optical images; phase information; rotation angle estimation; stereosatellite images; Adaptive optics; Buildings; Data mining; Estimation; Image edge detection; Optical imaging; Robustness; Rotation angle; Zernike moments (ZM); phase information;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
Type :
conf
DOI :
10.1109/IGARSS.2014.6947153
Filename :
6947153
Link To Document :
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