Unsupervised change detection model based on hybrid conditional random field for high spatial resolution remote sensing imagery

In this paper, an unsupervised change detection model based on hybrid conditional random field model (HCRF) is proposed for high spatial resolution (HSR) remote sensing imagery. Traditional random field based algorithms are mainly based on the analysis of the difference image which ignores the spatial-temporal change information of ground objects which is important in dealing with HSR imagery. Thus in HCRF, a new graph structure is designed to explore the correlation of corresponding ground objects from different times to get a better result. The unary potential is selected as the probabilistic result of change vector analysis (CVA), the pairwise potential is modeled to consider the contextual information of difference image and the similarity between objects from bi-temporal original images is considered using an object term. The proposed method is tested on two HSR data sets (IKONOS and QuickBird) and out performs some state-of-art algorithms.