Building Change Detection From Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index

In this study, urban building change detection is investigated, considering that buildings are one of the most dynamic structures in urban areas. To this aim, a novel building change detection approach for multitemporal high-resolution images is proposed based on a recently developed morphological building index (MBI), which is able to automatically indicate the presence of buildings from high-resolution images. In the MBI-based change detection framework, the changed building information is decomposed into MBI, spectral, and shape conditions. A variation of the MBI is a basic condition for the indication of changed buildings. Besides, the spectral information is used as a mask since the change of buildings is primarily related to the spectral variation, and the shape condition is then used as a post-filter to remove irregular structures such as noise and road-like narrow objects. The change detection framework is carried out based on a threshold-based processing at both the feature and decision levels. The advantages of the proposed method are that it does not need any training samples and it is capable of reducing human labor, considering the fact that the current building change detection methods are totally reliant on visual interpretation. The proposed method is evaluated with a QuickBird dataset from 2002 and 2005 covering Hongshan District of Wuhan City, China. The experiments show that the proposed change detection algorithms can achieve satisfactory correctness rates (over 80%) with a low level of total errors (less than 10%), and give better results than the supervised change detection using the support vector machine (SVM).