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

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

Content Provider	MDPI
Author	Lyu, Xuzhe Hao, Ming Shi, Wenzhong
Copyright Year	2020
Description	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.
Starting Page	678
e-ISSN	22209964
DOI	10.3390/ijgi9110678
Journal	ISPRS International Journal of Geo-Information
Issue Number	11
Volume Number	9
Language	English
Publisher	MDPI
Publisher Date	2020-11-15
Access Restriction	Open
Subject Keyword	ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Imaging Science Remote Sensing Dsm Srm Shape Context Similarity Model Building Change Detection
Content Type	Text
Resource Type	Article

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