A scene change detection framework for multi-temporal very high resolution remote sensing images

The technology of computer vision and image processing is attracting more and more attentions in recent years, and has been applied in many research areas like remote sensing image analysis. Change detection with multi-temporal remote sensing images is very important for the dynamic analysis of landscape variations. The abundant spatial information offered by very high resolution (VHR) images makes it possible to identify the semantic classes of image scenes. Compared with the traditional approaches, scene change detection can provide a new point of view for the semantic interpretation of land-use transitions. In this paper, for the first time, we explore a scene change detection framework for VHR images, with a bag-of-visual-words (BOVW) model and classification-based methods. Image scenes are represented by a word frequency with three kinds of multi-temporal learned 2 dictionary, i.e., the separate dictionary, the stacked dictionary, and the union dictionary. Three features (multispectral raw pixel; mean and standard deviation; and SIFT) and their combinations were tested in scene change detection. Post-classification and compound classification were evaluated for their performances in the “from-to” change results. Two multi-temporal scene datasets were used to quantitatively evaluate the proposed scene change detection approach. The results indicate that the proposed scene change detection framework can obtain a satisfactory accuracy and can effectively analyze land-use changes, from a semantic point of view.