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In pace with rapid urbanization, urban areas in many countries are undergoing huge changes. The large spectral variance and spatial heterogeneity within the ‘buildings’ land cover class, as well as the similar spectral properties between buildings and other urban structures, make building change detection a challenging problem. In this work, we propose a set of novel building change indices (BCIs) by combining morphological building index (MBI) and slow feature analysis (SFA) for building change detection from high-resolution imagery. MBI is a recently developed automatic building detector for high-resolution imagery, which is able to highlight building components but simultaneously suppress other urban structures. SFA is an unsupervised learning algorithm that can discriminate the changed components from the unchanged ones for multitemporal images. By effectively integrating the information from MBI and SFA, the building change components can be automatically generated. Experiments conducted on the QuickBird 2002–2005 data-set are used to validate the effectiveness of the proposed building change detection framework.
