Unsupervised change detection based on conditional random fields and texture feature for high resolution remote sensing imagery

In this paper, an unsupervised change detection method based on conditional random fields with texture feature (TFCRF) is designed for high spatial resolution (HSR) remote sensing images in order to make better use of the spatial information of HSR imagery. We firstly use the change vector analysis (CVA) method to calculate the difference image, and the texture features are extracted from the difference image with the help of gray level cooccurrence matrix (GLCM). Two initial change detection probabilistic maps are then acquired using the expectation maximization (EM) algorithm based on spectral and extracted texture information, respectively. Those two probabilistic maps are fused into the TFCRF algorithm using a probabilistic ensemble model to get the final binary change map. The experimental results on QuickBird and eCognition test images have shown the potential of the proposed TFCRF method in the field of change detection for HSR remote sensing images.