Review of multiscale geometric decompositions in a remote sensing context

Abstract

© 2016 SPIE and IS & T. The quest for optimal representations is considered a challenging goal in the field of image processing. This consists of reducing the processing's complexity while ensuring an efficient reconstruction. An optimal representation should conserve the properties of the image pertaining to smooth content and contours. The multiscale geometric decompositions (MGD) were designed to reach this finality. They were used in many fields and for different purposes, such as feature extraction, detail enhancing, and change detection. A state-of-art of these decompositions is proposed in this paper. We present their theoretical definitions and how they capture the feature of the objects within an image. An overview table is elaborated where we summarize the methods, the data and the different criteria of assessment used in the studied cases. We are interested, particularly, in the use of MGD in a remote sensing (RS) context. Thus, some examples of their applications on RS images are studied. A discussion is presented based on the analyzed cases.