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Journal of Applied Remote Sensing

Automatic true orthophoto generation based on three-dimensional building model using multiview urban aerial images
Author(s): Fei Deng; Junhua Kang; Penglong Li; Fang Wan
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Paper Abstract

The orthophoto with refined details and higher accuracy is important for urban geographical information system. The traditional differential rectification did not consider the height information of buildings when dealing with imagery over urban areas, resulting in buildings having relief displacement and cannot be located at their true geographical positions. In this study, a digital building model (DBM)-based procedure for automatic true orthophoto generation is proposed to solve this problem. This procedure includes three major steps: (1) traditional orthophotos generation, (2) buildings relief correction, and (3) occlusion detection and compensation. In our method, the relief displacements for buildings are corrected and occlusions are detected by using the backprojection and intersection method based on vector DBM surface polygon. True orthophotos are obtained with the compensation of occlusions. Experimental results show that the generated true orthophotos can achieve root-mean-square errors of 0.149 and 0.061 m on the <inline-formula< <mml:math display="inline" xmlns:mml=""< <mml:mrow< <mml:mi<X</mml:mi< </mml:mrow< </mml:math< </inline-formula<- and <inline-formula< <mml:math display="inline" xmlns:mml=""< <mml:mrow< <mml:mi<Y</mml:mi< </mml:mrow< </mml:math< </inline-formula<-axes, respectively. The planimetric positioning accuracy of the true orthophoto is around 1 pixel. This indicates that the proposed method can correctly remove the displacement caused by terrain and tall buildings, and the occluded areas can be detected and compensated effectively for generating true orthophotos with high quality.

Paper Details

Date Published: 11 March 2015
PDF: 15 pages
J. Appl. Remote Sens. 9(1) 095087 doi: 10.1117/1.JRS.9.095087
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Fei Deng, Wuhan Univ. (China)
Junhua Kang, Wuhan Univ. (China)
Penglong Li, Wuhan Univ. (China)
Fang Wan, Wuhan Univ. (China)

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