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Remote Sens. 2017, 9(1), 14; doi:10.3390/rs9010014

Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Jie Shan, Juha Hyyppä, Xiaofeng Li and Prasad S. Thenkabail
Received: 17 October 2016 / Revised: 29 November 2016 / Accepted: 22 December 2016 / Published: 27 December 2016
(This article belongs to the Special Issue Airborne Laser Scanning)
View Full-Text   |   Download PDF [13953 KB, uploaded 27 December 2016]   |  

Abstract

Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points. View Full-Text
Keywords: airborne LiDAR point clouds; building point extraction; building LoDs; the morphological scale space; point cloud segmentation airborne LiDAR point clouds; building point extraction; building LoDs; the morphological scale space; point cloud segmentation
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Yang, B.; Huang, R.; Li, J.; Tian, M.; Dai, W.; Zhong, R. Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space. Remote Sens. 2017, 9, 14.

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