Abstract
Abstract. 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.
Highlights
The robustness of the change detection system will be mainly guaranteed by the selection of the training sets that best discriminate each object of the DB from the rest of the image
Our method was applied on real 2D geographic DB and satellite images
One area of interest acquired with a satellite image is first described
Summary
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W2, 2013 ISA13 - The ISPRS Workshop on Image Sequence Analysis 2013, 11 November 2013, Antalya, Turkey or unsupervised clustering approaches; ⊲ As few changes exist between the DB and the image, the DB can be kept as initial solution to select reliable training sets for the classifier Such assumptions will be the basis of the solution proposed here. A specific classifier is trained on the subset of pixels, taken inside the object, able to best discriminate the object from the rest of the pixels of the image and out of the current theme The selection of such a training set allows to reduce registration issues between DB and images that is often reported in the literature (Poulain et al, 2009).
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