Mapping and updating maps in dense urban regions using high resolution digital airborne imagery, surface models and object-based classification

Abstract

Over the last couple of years more and more analogue airborne cameras were replaced by digital cameras. Digitally recorded image data show significant advantages to film based data. Digital aerial photographs exhibit a much better radiometric resolution. Image information can be acquired in shaded areas too. This information is essential for a stable and continuous classification, because no data or unclassified areas should be as small as possible. Considering this technological progress, one of the basic questions is how the potential of high radiometric and geometric resolution data can be used in an automatic analysis particularly in urban regions. For this study an object-based classification algorithm was selected to evaluate its suitability to update maps. In this project, image data of two digital airborne cameras were used. The High Resolution Stereo Camera - Airborne eXtended, HRSC-AX, an extended version of the HRSC-A and the Vexcel UltraCamD. Both cameras provide similar good and accurate image data and Digital Surface Model (DSM) data, but they cannot be directly compared, as both have different spectral and spatial characteristics and different pre-processing. Both resulting data sets were used in two separate analyses to develop and test an automated object-based classification procedure using the commercial software Definiens Developer. Taking the aim of map updating into account, emphasis was set on the delineation quality of buildings and correct detection of vegetation and impervious surfaces. As project area the centre of Berlin, Germany, was selected.