Fault-Tolerant Building Change Detection From Urban High-Resolution Remote Sensing Imagery

This letter proposes a novel change detection model, focusing on building change information extraction from urban high-resolution imagery. It consists of two blocks: 1) building interest-point detection, using the morphological building index (MBI) and the Harris detector; and 2) multitemporal building interest-point matching and the fault-tolerant change detection. The proposed method is insensitive to the geometrical differences of buildings caused by different imaging conditions in the multitemporal high-resolution imagery and is able to significantly reduce false alarms. Experiments showed that the proposed method was effective for building change detection from multitemporal urban high-resolution images. Moreover, the effectiveness of the algorithm was validated by comparing with the morphological change vector analysis (CVA), parcel-based CVA, and MBI-based CVA.