TY - JOUR
T1 - A quality prediction method for building model reconstruction using LiDAR data and topographic maps
AU - You, Rey Jer
AU - Lin, Bo Cheng
N1 - Funding Information:
Manuscript received December 24, 2009; revised April 5, 2010 and August 20, 2010; accepted February 20, 2011. Date of publication May 12, 2011; date of current version August 26, 2011. This work was supported in part by the National Science Council, Taiwan, under the Contracts NSC 98-2221-E-006-218 and 99-2221-E-006-182.
PY - 2011/9
Y1 - 2011/9
N2 - This paper integrates light detection and ranging (LiDAR) data and opographic maps and predicts the quality of 3-D building model reconstruction. In this paper, the tensor voting algorithm and a region-growing method are adopted to extract building roof planes and structural lines from LiDAR data, and a robust least squares method is applied to register LiDAR data with building outlines obtained from topographic maps. The minimal square sum of the separations of the most peripheral points to building outlines is adopted as the criterion for determining the transformation parameters in order to improve the efficiency of data fusion. After registration, a novel quality indicator of data fusion based on the tensor analysis of residuals is derived in order to evaluate the quality of the automatic reconstruction of 3-D building models. Finally, an actual LiDAR data set and its corresponding topographic map demonstrate the fusion procedure and the quality of the predictions related to automatic model reconstruction.
AB - This paper integrates light detection and ranging (LiDAR) data and opographic maps and predicts the quality of 3-D building model reconstruction. In this paper, the tensor voting algorithm and a region-growing method are adopted to extract building roof planes and structural lines from LiDAR data, and a robust least squares method is applied to register LiDAR data with building outlines obtained from topographic maps. The minimal square sum of the separations of the most peripheral points to building outlines is adopted as the criterion for determining the transformation parameters in order to improve the efficiency of data fusion. After registration, a novel quality indicator of data fusion based on the tensor analysis of residuals is derived in order to evaluate the quality of the automatic reconstruction of 3-D building models. Finally, an actual LiDAR data set and its corresponding topographic map demonstrate the fusion procedure and the quality of the predictions related to automatic model reconstruction.
UR - http://www.scopus.com/inward/record.url?scp=80052320656&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052320656&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2011.2128326
DO - 10.1109/TGRS.2011.2128326
M3 - Article
AN - SCOPUS:80052320656
SN - 0196-2892
VL - 49
SP - 3471
EP - 3480
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 9
M1 - 5766030
ER -