Building Change Detection Using a Shape Context Similarity Model for LiDAR data

ISPRS International Journal of Geo-Information

Wenzhong Shi¹  Ming Hao²  Xuzhe Lyu³ 

[1] Department of Land Surveying and Geo-Informatics, Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong 999077, China;NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;

关键词: DSM;    SRM;    shape context similarity model;    building change detection;   

DOI  :  10.3390/ijgi9110678

来源: DOAJ

【 摘 要 】

In this paper, a novel building change detection approach is proposed using statistical region merging (SRM) and a shape context similarity model for Light Detection and Ranging (LiDAR) data. First, digital surface models (DSMs) are generated from LiDAR acquired at two different epochs, and the difference data D-DSM is created by difference processing. Second, to reduce the noise and registration error of the pixel-based method, the SRM algorithm is applied to segment the D-DSM, and multi-scale segmentation results are obtained under different scale values. Then, the shape context similarity model is used to calculate the shape similarity between the segmented objects and the buildings. Finally, the refined building change map is produced by the k-means clustering method based on shape context similarity and area-to-length ratio. The experimental results indicated that the proposed method could effectively improve the accuracy of building change detection compared with some popular change detection methods.

【 授权许可】

Unknown