The elevation information is not considered in the traditional building change detection methods. This paper presents an algorithm of combining LiDAR data and ortho image for 3D building change detection. The advantages of the proposed approach lie in the fusion of the height and spectral information by thematic segmentation. Furthermore, the proposed method also combines the advantages of pixel-level and object-level change detection by image differencing and object analysis. Firstly, two periods of LiDAR data are filtered and interpolated to generate their corresponding DSMs. Secondly, a binary image of the changed areas is generated by means of differencing and filtering the two DSMs, and then thematic layer is generated and projected onto the DSMs and DOMs. Thirdly, geometric and spectral features of the changed area are calculated, which is followed by decision tree classification for the purpose of extracting the changed building areas. Finally, the statistics of the elevation and area change information as well as the change type of the changed buildings are done for building change analysis. Experimental results show that the completeness and correctness of building change detection are close to 81.8% and 85.7% respectively when the building area is larger than 80 <i>m</i><sup>2</sup>, which are increased about 10% when compared with using ortho image alone.
Object-level change detection in spectral imagery
