Unsupervised Scene Change Detection via Latent Dirichlet Allocation and Multivariate Alteration Detection

Abstract

Scene change detection is the process of identifying the differences between the multitemporal image scenes, which has significant potential in the application of urban development and land management at the semantic level. Traditional scene change detection methods are based on the supervised scene classification, and then directly compare the independent classification results without considering the temporal correlation between the unchanged regions. However, few studies have focused on detecting the semantic changes of multitemporal image scenes with unsupervised methods. In this paper, we propose a novel unsupervised scene change detection method by using latent Dirichlet allocation (LDA) and multivariate alteration detection (MAD). First, the scene is represented by the bag-of-visual-words model, and adopt the union dictionary to ensure the consistency of dictionary space. Then, LDA is used to achieve the middle-level feature dimension reduction, and generate the common topic space of the two multitemporal image scene datasets. And finally, the MAD method was applied to detect the semantic changes of corresponding multitemporal image scenes. Two experiments with high-resolution remote sensing image scene datasets demonstrated that our proposed approach can get a better performance in unsupervised scene change detection without prior knowledge.