A morphology-based method for building change detection using multi-temporal airborne LiDAR data

作者：Xi, Yicheng; Luo, Qingli^*

来源：Remote Sensing Letters, 2018, 9(2): 131-139.

DOI：10.1080/2150704X.2017.1402384

• URBAN AREAS
• POINT CLOUDS
• SEGMENTATION

The detection of building changes plays an important role for land management and urban planning. This study proposes an automatic method that applies the morphology-based method for detection of building changes with multi-temporal airborne Light Detection and Ranging (LiDAR) data. In this method, three types of land use are classified as ground, building and vegetation by Triangulated Irregular Network (TIN) filter and Support Vector Machine (SVM) with the two sets of point clouds acquired at different times. Thereafter, the morphological algorithm is applied to distinguish the changed buildings from trees, which significantly reduces the identification of errors. The changed building objects are classified as three types: 'newly built', 'taller' and 'demolished', with the use of rule analysis. The case study area is located in Liujia village, Shenyang, China. The two sets of LiDAR test data were acquired by Leica ALS 60 (3 points/km(2)) and ALS 80 (15 points/km(2)) from September 2014 and February 2017, respectively. Field investigation proves that the accuracy of the identified changed buildings of 'newly built', 'demolished' and 'taller' are 95.4%, 92.9% and 100%. The proposed method has the advantages of high accuracy and reliability, which will provide a reference for the detection of building changes.

• 出版日期2018
• 单位天津大学; 沈阳农业大学