Object-based feature extraction and semi-supervised classification for urban change detection using high-resolution remote sensing images

This paper presents a novel approach for urban change detection of high resolution (HR) remote sensing images. To overcome deficiency of traditional pixel-based methods and better annotate HR images, object-based strategies are adopted. Firstly change vector analysis (CVA) and local binary patterns (LBP) are utilized to extract the object-specific features based on the image-objects acquired by multitemporal segmentation. Then sparse representation is further exploited to characterize highly effective sparse features. Finally, the final change map is obtained by support vector machine (SVM) with the pseudotraining set acquired by expectation maximization (EM). Comparative experiments demonstrate the effectiveness of the proposed method.