Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Remote Sensing

Sudan Xu¹  George Vosselman²  Sander Oude Elberink²  Juha Hyyppä² 

[1] Department of Earth Observation Science, Faculty ITC, University of Twente, PO BOX 217, 7500 AE Enschede, The Netherlands

关键词: building;    change;    classification;    ALS;    change detection;    airborne laser scanning;   

DOI  :  10.3390/rs71215867

来源: mdpi

【 摘 要 】

The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are “changed”, “unchanged”, or “unknown”, and quantifying the changes. The designation “unknown” is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between “changed”, “unchanged”, and “unknown”. Rules are defined to solve problems caused by the lack of data. Further, points labelled as “changed” are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%–90% of real changes are found, of which approximately 50% are considered relevant.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.