Abstract
Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas) has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.
Highlights
Buildings are the most significant component of the urban scence
In spite of the design level of the airborne light detection and ranging (LiDAR) equipment is continuously improved in recent years, the ground resolution of the point cloud still falls behind the aerial images when acquiring data at a similar height
In order to better meet the requirements of building contour refinement, some improvements are made for the traditional Snake model in our approach
Summary
Buildings are the most significant component of the urban scence. The location information of buildings is widely applied in many fields, such as urban planning, land investigation, change detection and military reconnaissance. In spite of the design level of the airborne LiDAR equipment is continuously improved in recent years, the ground resolution of the point cloud still falls behind the aerial images when acquiring data at a similar height This difference means that even if the building detection results are completely correct in LiDAR, there is still some room for the accuracy improvement of the contours, which makes the refinement of LiDAR-derived building contours a problem of concern. Compared with the abovementioned building contour processing methods, the advantage of the Snake model lies in the ability of integrating the input data, initial estimation, optimal contour generation and knowledge-based constraint into a single process It makes this model applied widely in building contour extraction and optimization.
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