Remote sensing image change detection using a hybrid graphical model

Abstract

Semisupervised graph learning has a broad prospect in remote sensing (RS) image change detection. However, an improper graph model may result in a contradiction between the detection accuracy and computational efficiency. In order to effectively extract the structural information of changes and heavily reduce the computational burden, we propose a hybrid graphical model (HGM) for bitemporal RS image change detection. The HGM utilizes the hybrid superpixels (HSPs) as its vertices, and a hybrid graph kernel (HGK) function is proposed for measuring the similarities between the vertices. The HSPs are composed of the background superpixels and foreground isolated pixels of a subtraction image. The HGM effectively exploits the image structures, and the small graph size dramatically reduces the computational complexity. Moreover, the piecewise HGK function well detects the structures of the changed areas and heavily resists the background disturbances. A semisupervised label propagation algorithm is implemented with the HGK matrix for obtaining the final change detection results. Experimental results on real RS images demonstrate the effectiveness and efficiency of the proposed method and prove that it is a good candidate for RS image change detection.