TOWARDS THE AUTOMATIC DETECTION OF GEOSPATIAL CHANGES BASED ON DIGITAL ELEVATION MODELS PRODUCED BY UAV IMAGERY
Reliable and accurate geospatial-databases (Digital Elevation Models, DEMs) are an essential component of Geographic Information Systems (GIS). One of their most important uses is change detection – an invaluable tool for environmental interpretation and evidence-based action. High-performance and inexpensive Unmanned Aerial Vehicles (UAVs) are increasingly used for the acquisition of timely geospatial information (imagery) for the production of DEMs for geospatial change detection. DEMs produced from UAV imagery have very high resolution and very good internal accuracy. However, their absolute location accuracy is inferior to other mapping technologies. Therefore, existing change detection methods, which are based on the point-by-point comparison, will perform poorly when processing DEMs created from UAV imagery since they are limited in reliably separating real physical changes from artifacts related to DEM inherent inaccuracy or errors. This paper presents a novel methodology that overcomes these deficiencies, by implementing a hierarchical analysis and modeling process, in which a sequence of methods is used to automatically identify and match unique homological features, such as building corners or topographic maxima, in the various height models. These provide geospatial anchors that bring out local geospatial discrepancies between the models. Those are then used to "repair" (align) the models to the same geospatial reference system, at which point change-detection is performed. Experimental results showed that when calculating point-by-point height differences, 98.99% of the area was falsely classified as changed, whereas implementing our method adequately detected all the actual changes in the area with no false positives, correctly classifying 0.16% of the area as changed.