Updating land cover databases using a single very high resolution satellite image
Image change detection has been extensively tackled in the literature in various domains, and in particular for remote sensing purposes. Indeed, very high resolution geospatial images are nowadays ubiquitous and can be used to update existing 2D and 3D geographical databases. Such databases can be projected into the image space, by a rasterization step. Therefore, they provide 2D label maps that can be subsequently compared with classifications resulting for geospatial image processing. In this paper, we propose a classificationbased method to detect changes between label maps created from 2D land-cover databases and an more recent orthoimage, without any prior assumptions about the databases composition. Our supervised method is based both on an efficient training set selection and a hierarchical decision process, that follows the structure of topographical databases. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates, a standard limitation of existing approaches. The designed framework is successfully applied on very high resolution images of Pl´eiades sensor and two distinct national land-cover databases.