Multitemporal 3D Change Detection in Urban Areas Using Stereo Information from Different Sensors

Abstract

For a detailed monitoring of cities only very high resolution (VHR) satellite images as well as airborne imagery are able to provide the required accuracy. Especially not well mapped cities and new build up areas and their infrastructure like buildings, streets, bridges and other objects are of interest. Just the 2D information delivered by single images is often not sufficient when dealing with changes. Therefore stereo imagery is of importance to derive the height component which is of high value in analyzing 3D objects. The paper describes 3D change detection methodology if several Digital Surface Models (DSM) from different dates and sensors are available and provides solutions by the joint use of height and spectral information. The subtraction of DSMs, computed from image pairs acquired at different dates, should give direct results about changes. However, when at least one of the DSMs presents artifacts, a simple DSM subtraction often results in the detection of a combination of real and virtual changes. Therefore morphological based post-processing steps are proposed and adapted to different DSM qualities in order to remove artificial changes. In addition to height information, vegetation and shadow masks are generated, which are of great help in reconstructing sharper and more correct building boundaries. The used data are acquired over the city of Munich, Germany. They consist of a time series of stereo images acquired within the years 2005 to 2010 from space borne (IKONOS and Worldview) and airborne (3K-Camera-System) sensors. Evaluation of the proposed approaches in terms of false alarm, missed alarm and Kappa Coefficient etc is performed, proving its efficiency and relatively high accuracy for different kinds and combinations of stereo images and consequently different DSM qualities.