Hayet Si Salah

Remote sensing change detection (RSCD) is the process of identifying changes between scenes of the same location acquired at different times. This is an active research area with a broad range of applications. Many change detection methods have been described in the literature, ranging from simple differencing to machine learning techniques. Faced with this large number of suggested techniques, researchers have tried to classify them into different categories to simplify and systematize the topic. A number of different categorization schemes have been proposed, based on various dimensions, aspects or features of the techniques considered. Nevertheless, using these schemes to understand the different techniques and to select suitable techniques for a specific remote sensing change detection project remains a hard task for practitioners. In this context, we provide a critical study of proposed categorization schemes in order to extract the main dimensions used for their construction. Then, based on the obtained set of dimensions, we propose a new definition for the concept of a remote sensing change detection technique, which provides greater clarity and better guidance for researchers and practitioners. We then expand upon this description to incorporate non-computational aspects of RSCD in order to propose a comprehensive conceptual framework for remote sensing change detection projects.

Change detection (CD) from remote sensing data is a very challenging research problem, especially when we analyse an urban scene. Urban scenes are composed of many different types of objects, both natural and man-made. The building class is one of the important and most complex classes to analyse, important because it is useful for so many applications and complex because it exhibits many changes due to human activity and natural catastrophes. For these reasons, we focus our study on building change detection (BCD). In this paper we propose a classification scheme for BCD research according to several important dimensions including objective, input data, temporal resolution, analysis unit, target output unit, building features, processing technique, change categories, and assessment of results. This classification scheme can guide practitioners in choosing appropriate change detection methods to achieve their goals as well as informing new research efforts. Based on this multidimensional characterisation of BCD, we offer a number of suggestions for further work to be done in this field.