Cosegmentation for Object-Based Building Change Detection From High-Resolution Remotely Sensed Images

This paper presents a cosegmentation-based method for building change detection from multitemporal high-resolution (HR) remotely sensed images, providing a new solution to object-based change detection (OBCD). First, the magnitude of a difference image is calculated to represent the change feature. Next, cosegmentation is performed via graph-based energy minimization by combining the change feature with image features at each phase, directly resulting in foreground as multitemporal changed objects and background as unchanged area. Finally, the spatial correspondence between changed objects is established through overlay analysis. Cosegmentation provides a separate and associated, rather than a separate and independent, multitemporal image segmentation method for OBCD, which has two advantages: 1) both the image and change features are used to produce foreground segments as changed objects, which can take full advantage of multitemporal information and produce two spatially corresponded change detection maps by the association of the change feature, having the ability to reveal the thematic, geometric, and numeric changes of objects and 2) the background in the cosegmentation result represents the unchanged area, which naturally avoids the problem of matching inconsistent unchanged objects caused by the separate and independent multitemporal segmentation strategy. Experimental results on five HR datasets verify the effectiveness of the proposed method and the comparisons with the state-of-the-art OBCD methods further show its superiority.