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

Other literature type, Article English OPEN
Xu, Sudan ; Vosselman, George ; Elberink, Sander Oude (2015)

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.
  • References (18)
    18 references, page 1 of 2

    1. Xiao, W.; Vallet, B.; Paparoditis, N. Change detection in 3D point clouds acquired by mobile mapping system. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2013, 2, 331-336. [CrossRef]

    2. Xu, S.; Oude Elberink, S.; Vosselman, G. Multiple-entity based classification of airborne laser scanning data in urban areas. ISPRS J. Photogramm. Remote Sens. 2013, 88, 1-15. [CrossRef]

    3. Mas, J.-F. Monitoring land-cover changes: A comparison of change detection techniques. Int. J. Remote Sens. 1999, 20, 139-152. [CrossRef]

    4. Byrne, G.F.; Crapper, P.F.; Mayo, K.K. Monitoring land-Cover change by principle component analysis of multitemporal landsat data. Remote Sens. Environ. 1980, 10, 175-184. [CrossRef]

    5. Sharma, B.; Rishabh, I.; Rakshit, S. Unsupervised change detection using RANSAC. In Proceedings of the IET International Conference on Visual Information Engineering, Bangalore, India, 26-28 September 2006; pp. 24-28.

    6. Yang, Z.; Qin, Q.; Zhang, Q. Change Detection in high spatial resolution images based on support vector machine. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Denver, CO, USA, 31 July-4 August 2006; pp. 225-228.

    7. Di, F.; Li, X.; Zhu, C. A new method in change detection of remote sensing image. In Proceedings of the 2nd International Congress IEEExplore Digital Library, Image and Signal Processing (CISP' 09), Tianjin, China, 17-19 October 2009; pp. 1-4.

    8. Tanathong, S.; Rudahl, K.T.; Goldin, S.E. Object oriented change detection of buildings after a disaster. In Proceedings of the ASPRS 2009 Annual Conference, Baltimore, MD, USA, 9-13 March 2009. On CD-Rom.

    9. Kasetkasem, T.; Varshney, P.K. An Image Change detection algorithm based on Markov Random Field models. IEEE Trans. Geosci. Remote Sens. 2002, 40, 1815-1823. [CrossRef]

    10. Murakami, H.; Nakagawa, K.; Hasegawa, H.; Shibata, T.; Iwanami, E. Change detection of buildings using an airborne laser scanner. ISPRS J. Photogramm. Remote Sens. 1999, 54, 148-152. [CrossRef]

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