The aim of this application is to detect changes in an aerial scene by comparing stereo pairs taken at intervals of several years in order to update a database. The result is a set of image locations that have a high likelihood to contain changes. Each location will be submitted to a human operator who will either validate the given change and update the database or reject it. We are mainly interested in changes occurring for a specific class of objects : buildings. To isolate new construction, we provide an algorithm that works in two steps. First, during a focusing phase, we aim to eliminate a large part of the scene without losing any actual changes. This is achieved with a Digital Elevation Model (DEM) comparison between the two different dates. Then, in the second phase, we classify regions of interest (ROI). Each ROI is described by four images: a stereo pair of the focusing area at the first date and a stereo pair of the focusing area at the second date. To decide whether or not the ROI contains a change, we classify each of the four images as building or non-building. The building vs non-building classifier is a combination of several decision trees induced by learning. Each node of a decision tree is identified with a graph of features which is more likely to describe buildings than background. Finally, the classification results at the two different dates are compared.
"Detecting new buildings from aerial stereo pairs at different dates", Proc. SPIE 4472, Applications of Digital Image Processing XXIV, (7 December 2001); doi: 10.1117/12.449758; https://doi.org/10.1117/12.449758